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refarch-cognitive-discovery-broker | watson discovery broker service this project is part of the ibm cognitive reference architecture cyan compute validation available at https github com ibm cloud architecture refarch cognitive updated 05 24 2018 angular 6 new watson api table of contents introduction introduction what you will learn what you will learn from this project run and test locally run and test locally demonstration script docs demo script md deploy deploy deep dive tutorial for watson discovery docs tutorial wds lab md code explanation docs broker code md faq docs faq md introduction this project implements a microservice deployable as a containerized application on ibm cloud or ibm cloud private to facade watson discovery service to address integration requirements there are always requirements for data mapping expose special user interface or to do service orchestration the value of this broker code is to support resiliency service management logging and other integrations therefore this broker server exposes rest api that user interface born on cloud or any application like ibm bpm coaches can consume it uses a node js express js backend to host the static content and implements the bff backend for frontend pattern the concept of broker is presented in the ibm cognitive reference architecture for engagement and discovery as illustrated in the figure below as the discovery application icon wds reference architecture docs wds ra png current version the current version may 2018 has the following features angular 6 single page application user interface to present watson news collection query and to support the discovery training https www ibm com cloud garage architectures cognitivediscoverydomain create query data collection watson discovery we developed in 2017 for weather collection backend for frontend application to serve the angular app and to offer a set of apis for the search within discovery collections distributed as docker container and deployed to kubernetes cluster on ibm cloud container or ibm cloud private integrate watson developer api for discovery you can fork it for your own purpose and develop your own solution by reusing the code if you want to contribute please submit a pull request on this repository the contribution guidelines are here https github com ibm cloud architecture refarch cognitive contribute what you will learn from this project watson discovery creation and development activities watson discovery api and how to integrate within a web application or a micro service how to add resiliency to your broker how to present the discovery results in angular 2 spa run and test locally you need to have your local development environment properly configured with all the tools installed for example angular cli node docker and kubernetes command line tool if needed we packaged a shell to download all the cli needed see this section https github com ibm cloud architecture refarch cognitive build and run locally each application skill set to understand how to build a discovery collection the following tutorial docs tutorial wds lab md will help to go step by step with weather related corpus the audience of this tutorial is for beginner and developer as it supports different knowledge level from simple to deeper dive if you do not have git install git on mac by installing the command line tools for xcode to do this open a terminal and execute the following command xcode select install on windows download and install the package from https git for windows github io and install it create a watson discovery service on ibm cloud to be able to run this watson discovery broker you need your own instance of a ibm cloud watson discovery service see the tutorial docs tutorial wds lab md for how to do so once you have created this service rename the server routes env templ json to env json and modify the userid password environment id and collection id to point to your own service be sure to have setup cloud foundry command line interface and ibm cloud cli clone the repository to your local machine to clone this repository to your local machine please use git or a graphical tool like sourcetree example for git go to a directory where you want the source to be created in clone the repo mkdir stsa watsondiscovery cd stsa watsondiscovery git clone https github com ibm cloud architecture refarch cognitive discovery broker git cd refarch cognitive discovery broker build be sure to run the npm installation to get the dependent javascript modules to update npm tool globally sudo npm i g npm install the following package globally too sudo npm i g node sudo npm i g node gyp 3 6 2 sudo npm i g angular cli latest then install the package from package json npm install run ng build to build the client angular 2 project the build artifacts will be stored in the dist directory use the prod flag for a production build if you want to work on the user interface only you can use the command below this is for ui work only and will not successfully connect to the ibm cloud instance ng serve so when we need to have the real server running perform the next step test locally with the bff server be sure to have setup the content of the file config json file under the server routes config folder to do so rename the config tmpl json file in server routes config to config json and open it with a text editor the environment information can be found in collection screen in watson discovery service collection information docs discovery screen1 png and from the service credentials screen in your ibm cloud dashboard credentials information docs bmx service credentials1 png add the username password environment id collection id and configuration id parameters where needed do not change the attributes name as they are used to load the data in the broker discovery username password version date 2018 03 05 version v1 environment id collection id weathercollection username password version date 2018 03 05 version v1 environment id collection id configuration id to start the local bff server use the following command npm start if you run into any npm dependency issues or missing modules add them using the command normally the package json is accurate and validated as of may 2018 npm install missing module and rerun the command to start the server npm start finally use a web browser to http localhost 6010 which should display a home page with different choices the port number may be different consult the server trace simple query docs wds ui home png see the demonstration script for information about running the demo here docs demo script md deploy we have two choices using cloud foundry or using docker container to be deployed on kubernetes cluster we recommend the second choice using cloud foundry we will not go over the detail on how to create a cloud foundry application in ibm cloud as it is covered a lot within other ibm cloud blogs but you need to do the following steps using cloud foundry command line interface login to your region organization and space cf login ensure the file manifest yml reflects the name of the host and url you have configured in ibm cloud nodejs cloud application applications path memory 256m instances 1 domain mybluemix net name refarch wds broker host refarch wds broker disk quota 1024m perform a push with the name of the application for example cf push refarch cognitive discovery broker validate the deployment on your ibm cloud dashboard and using the defined url deploy to kubernetes cluster as of june 2017 the cyan compute uses ibm cloud kubernetes service and containerized application like this discovery broker microservice so it can be deployed to public and private cloud platform you can read detail in this note docs wds broker kube md code explanation to get detail on how the code is working and organized see the node here docs broker code md frequent asked questions they are in a separate document here docs faq md | 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Python-for-Probability-Statistics-and-Machine-Learning | jupyter notebooks for springer book python for probability statistics and machine learning https www springer com fr book 9783319307152 draft cover python for probability statistics and machine learning jpg note second edition updated for python 3 6 is now available https www springer com gp book 9783030185442 with corresponding jupyter notebooks https github com unpingco python for probability statistics and machine learning 2e | jupyter-notebook python machine-learning book books probability probability-theory statistics statistics-course statistical-analysis statistical-learning statistical-tests | ai |
pytorch-nlp-tutorial-ny2019 | o reilly artificial intelligence conference natural language processing with deep learning training delip rao brian mcmahan please visit dl4nlp info http dl4nlp info en latest for more information about the current training session docker instructions start from the root directory which contains the dockerfile please run the following commands replacing localport with whatever you d like data should come with repo so no need to download fresh docker build t dl4nlp docker run p localport 8888 d dl4nlp if running outside of docker can do the following from root dir where you can see day 1 day 2 etc jupyter notebook notebook dir pwd assuming all necessary things are installed required python packages are in requirements txt except for pytorch follow the installation instructions on the pytorch website also visit dl4nlp info http dl4nlp info en latest for more information about the current training session and nlproc info http nlproc info for more resources from us | ai |
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data-engineering-on-gcp | data engineering on gcp data engineering on google cloud platform gcp | cloud |
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MM-BigBench | mm bigbench evaluating multimodal models on multimodal content comprehension tasks paper https arxiv org abs 2310 09036 data https github com declare lab mm bigbench tree main multimodal data p align center img src figure mm bigbench png alt width 200 height 300 p why the popularity of multimodal large language models mllms has triggered a recent surge in research efforts dedicated to evaluating these models nevertheless existing evaluation studies of mllms such as mme https arxiv org abs 2306 13394 seed bench https arxiv org abs 2307 16125 lvlm ehub https arxiv org abs 2306 09265 and mm vet https arxiv org abs 2308 02490 primarily focus on the comprehension and reasoning of unimodal vision content neglecting performance evaluations in the domain of multimodal vision language content understanding beyond multimodal reasoning tasks related to multimodal content comprehension necessitate a profound understanding of multimodal contexts achieved through the multimodal interaction to obtain a final answer in this project we introduce a comprehensive assessment framework called mm bigbench which incorporates a diverse range of metrics to offer an extensive evaluation of the performance of various models and instructions across a wide spectrum of diverse multimodal content comprehension tasks including multimodal sentiment analysis msa multimodal aspect based sentiment analysis mabsa multimodal hateful memes recognition mhmr multimodal sarcasm recognition msr multimodal relation extraction mre and the visual question answering vqa with text context consequently our work complements research on the performance of mllms in multimodal comprehension tasks achieving a more comprehensive and holistic evaluation of mllms mm bigbench with a range of diverse metrics to provide a thorough evaluation of different models and instructions including the best performance metric the mean relative gain metric the stability metric and the adaptability metric evaluated models 14 mllms model name modality model code paper plm pvm total paras training paras chatgpt text chatgpt https openai com blog chatgpt paper https arxiv org abs 2303 08774 gpt 3 5 turb llama1 7b text llama 1 https github com facebookresearch llama tree llama v1 paper https arxiv org abs 2302 13971 llama v1 7b 6 74b 6 74b llama1 13b text llama 1 https github com facebookresearch llama tree llama v1 paper https arxiv org abs 2302 13971 llama v1 13b 13 02b 13 02b llama2 7b text llama 2 https github com facebookresearch llama and llama recipes https github com facebookresearch llama recipes paper https arxiv org abs 2307 09288 llama v2 7b 6 74b 6 74b llama2 13b text llama 2 https github com facebookresearch llama and llama recipes https github com facebookresearch llama recipes paper https arxiv org abs 2307 09288 llama v2 13b 13 02b 13 02b flan t5 xxl text flan t5 xxl https huggingface co google flan t5 xxl paper https arxiv org abs 2210 11416 flan t5 xxl 11 14b 11 14b openflamingo multimodal openflamingo https github com mlfoundations open flamingo paper https openreview net forum id ebmuimabpbs llama 7b vit l 14 8 34b 1 31b fromage multimodal fromage https github com kohjingyu fromage paper https dl acm org doi 10 5555 3618408 3619119 opt 6 7b vit l 14 6 97b 0 21b llava 7b multimodal llava 7b https github com haotian liu llava paper https arxiv org abs 2304 08485 llama 7b vit l 14 6 74b 6 74b llava 13b multimodal llava 7b https github com haotian liu llava paper https arxiv org abs 2304 08485 llama 13b vit l 14 13 02b 13 02b minigpt4 multimodal minigpt4 https github com vision cair minigpt 4 paper https arxiv org abs 2304 10592 vicuna 13b vit g 14 14 11b 0 04b mplug owl multimodal mplug owl https github com x plug mplug owl paper https arxiv org abs 2304 14178 llama 7b vit l 14 7 12b 7 12b llama adapter v2 multimodal llama adapter v2 https github com zrrskywalker llama adapter paper https www arxiv vanity com papers 2304 15010 llama 7b vit l 14 7 23b 7 23b vpgtrans multimodal vpgtrans https github com vpgtrans vpgtrans paper https arxiv org abs 2305 01278 vicuna 7b 7 83b 0 11b multimodal gpt multimodal multimodal gpt https github com open mmlab multimodal gpt paper https arxiv org abs 2305 04790 llama 7b vit l 14 8 37b 0 02b lavin 7b multimodal lavin 7b https github com luogen1996 lavin paper https arxiv org abs 2305 15023 llama 7b vit l 14 7 17b 7 17b lavin 13b multimodal lavin 13b https github com luogen1996 lavin paper https arxiv org abs 2305 15023 llama 13b vit l 14 13 36b 13 36b lynx multimodal lynx https github com bytedance lynx llm paper https arxiv org abs 2307 02469 vicuna 7b eva vit 1b 8 41b 0 69b blip 2 multimodal blip 2 https github com salesforce lavis tree main projects blip2 paper https arxiv org abs 2301 12597 flant5 xxl vit g 14 12 23b 0 11b instructblip multimodal instructblip https github com salesforce lavis tree main projects instructblip instructblip towards general purpose vision language models with instruction tuning paper https arxiv org abs 2305 06500 flant5 xxl vit g 14 12 31b 0 45b note refer to setting models multimodal eval main models readme md for you setup install dependencies and download data conda create n mm bigbench python 3 8 y conda activate mm bigbench pip install r requirements txt fast start select evaluated dataset model prompt type 1 select the evaluated task and dataset in the sh file 2 select the model to be evaluated in the sh file 3 select the prompt type from 1 to 10 in the sh file 4 run the corresponding sh file running the inference of difffernt models for chatgpt llama v1 7b llama v1 13b llama v2 7b llama v2 13b text flant5 xxl blip 2 instructblip fromage openflamingo multimodal gpt mplug owl minigpt4 llama adapterv2 vpgtrans llava 7b llava 13 models sh test scripts run scripts sh change the model name in the sh file to corresponding to the chatgpt decapoda llama 7b hf decapoda llamab hf meta llama2 7b hf meta llama2 13b hf text flan t5 xxl blip2 t5 blip2 instruct flant5xxl fromage openflamingo mmgpt mplug owl minigpt4 llama adapterv2 vpgtrans llava 7b llava 13b for lavin model sh test scripts run lavin zero shot sh for lynx model sh test scripts run lynx llm zero shot sh eval the results to get the accuracy metric sh test scripts eval scripts sh diverse metrics metrics used in our paper can be found in evaluation metrics evaluation citation bibtex inproceedings yang2023mmbigbenchem title mm bigbench evaluating multimodal models on multimodal content comprehension tasks author xiaocui yang and wenfang wu and shi feng and ming wang and daling wang and yang li and qi sun and yifei zhang and xiaoming fu and soujanya poria year 2023 url https api semanticscholar org corpusid 264127863 | multimodal-large-language-models multimodal-content-comprehension-tasks | ai |
tasks-manager | tasks manager tasks manager is a simple web application built with vue js and flask that allows users to create update and delete tasks the backend is written in python using flask and sqlite for the database the frontend is built with vue js and communicates with the backend using axios making this project in order to start learning web development with python and vue js | server |
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Data-Scientist-Books | data scientist books book lists image https user images githubusercontent com 74346775 150431744 12d89a5c abe5 4d6d ab17 3d6be41ce8e6 png image https user images githubusercontent com 74346775 150431808 91138d53 4512 4b6e 8995 f8eb14955a51 png image https user images githubusercontent com 74346775 150431892 e900fe9e 3bfa 4b6a b9a7 3e8029112116 png image https user images githubusercontent com 74346775 150431947 668d77f7 0946 474b b7bc 912aa2065400 png image https user images githubusercontent com 74346775 151635784 5007afec 12a4 4850 a577 e2c0fd641516 png image https user images githubusercontent com 74346775 150432021 5abfbd2b fdb0 46b0 a9e7 642b932f01eb png image https user images githubusercontent com 74346775 150432052 ad9f4d23 7553 4c22 adfc 2bb99d676db8 png image https user images githubusercontent com 74346775 150432275 a6f0fe77 b402 44ed 8800 98c617e98359 png image https user images githubusercontent com 74346775 151635715 5dfc7850 d06f 4965 becb bf739bf34a65 png | ai artificial-intelligence deeplearning machinelearning datascience dl ds ml gans lstm python r tsf probability statistics | ai |
blendocv | logotype http blendocv damiles com wp content themes damiles img logo png important note please be patient there are a lot of work to do before i release first beta versions and a how to compile blendocv i know that there are a lot of people that want compile and test blendocv but now there are only few nodes and we are working in make it stable and creating hundred of nodes for you my wish is share a first beta in mid march thanks for your understanding description blendocv is a new tool for computer vision prototyping based on two of best open source software blender and opencv blendocv is a node based tool for create your computer vision applications in real time without compile and viewing instantly the results logotype http blendocv damiles com wp content uploads 2013 12 bocv jpg video demo screenshot http img youtube com vi 1aah1cmlguc 0 jpg http youtu be 1aah1cmlguc official website http blendocv damiles com table of content how to compile todo http damiles com todo png todo tutorials todo http damiles com todo png todo how to get involved todo http damiles com todo png todo project status progress https github com damiles blendocv wiki project status development notes https github com damiles blendocv wiki development notes features fast prototyping node based system real time viewer multiple os flexibel user interface hundred of computer vision nodes image analysis tools ocr integration python extensions code generation text editor integrated and more license the gpl version 2 copyright c 2014 david mill n escriva damiles permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software software based on blender and opencv this software is a merge of this opensource softwares | ai |
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AIrsenal | airsenal build status https github com alan turing institute airsenal actions workflows main yml badge svg airsenal is a package for using machine learning to pick a fantasy premier league team for some background information and details see https www turing ac uk research research programmes research engineering programme articles airsenal we welcome contributions and comments if you d like to join the airsenal community please refer to our contribution guidelines https github com alan turing institute airsenal blob master contributing md mini league for 2023 24 season we have made a mini league prem ai league for players using this software to join login to the fpl website and navigate to the page to join a league https fantasy premierleague com leagues create join then click join a league or cup the code to join is uke1z3 hope to see your ai team there our own airsenal team s id for the 2023 24 season is 1822891 https fantasy premierleague com entry 1822891 history installation we recommend running airsenal in a conda environment for instructions on how to install conda go to this link https docs anaconda com anaconda install or the more lightweight miniconda https docs conda io en latest miniconda html with conda installed run these commands in a terminal to create a new conda environment and download and install airsenal linux and macos shell git clone https github com alan turing institute airsenal git cd airsenal conda env create conda activate airsenalenv windows the best ways to run airsenal on windows are either to use windows subsystem for linux https docs microsoft com en us windows wsl install wsl which allows you to run airsenal in a linux environment on your windows system or docker see below after installing wsl if you d like to use airsenal with conda run the following commands to install it from your wsl terminal following the linux instructions here https docs conda io en latest miniconda html linux installers shell wget https repo anaconda com miniconda miniconda3 latest linux x86 64 sh bash miniconda3 latest linux x86 64 sh you can then follow the installation instructions for linux and macos above or the instructions for without conda below you re free to try installing and using airsenal in windows itself but so far we haven t got it working the main difficulties are with installing jax https github com google jax installation and some database pickling errors e g 165 if you do get it working we d love to hear from you use airsenal without conda to use airsenal without conda shell git clone https github com alan turing institute airsenal git cd airsenal pip install pygmo linux only pip install airsenal has an optional optimisation algorithm using the pygmo package which is only pip installable on linux either use conda or don t install pygmo on other platforms however we have also occasionally seen errors when using conda e g 81 https github com alan turing institute airsenal issues 81 docker build the docker image console docker build t airsenal if docker build fails due to a runtimeerror like console unable to find installation candidates for jaxlib 0 4 11 this may be a lack of maintained versions of a package for m1 on linux a slow solution for this error is to force a linux amd64 build like console docker build platform linux amd64 t airsenal if that fails try console docker build platform linux amd64 no cache t airsenal see ticket 547 https github com alan turing institute airsenal issues 574 for latest on this issue create a volume for data persistance console docker volume create airsenal data run commands with your configuration as environment variables eg console docker run it rm v airsenal data tmp e fpl team id your id e airsenal home tmp airsenal bash or console docker run it rm v airsenal data tmp e fpl team id your id e airsenal home tmp airsenal airsenal run pipeline airsenal run pipeline is the default command optional dependencies airsenal has optional dependencies for plotting running notebooks and an in development airsenal api to install them run shell pip install api notebook plot configuration once you ve installed the module you will need to set the following parameters required 1 fpl team id the team id for your fpl side optional 2 fpl login your fpl login usually email this is only required to get fpl league standings or automating transfers via the api 3 fpl password your fpl password this is only required to get fpl league standings or automating transfers via the api 4 fpl league id a league id for fpl this is only required for plotting fpl league standings 5 airsenal db file local path to where you would like to store the airsenal sqlite3 database if not set airsenal home data db will be used by default the values for these should be defined either in environment variables with the names given above or as files in airsenal home a directory airsenal creates on your system to save config files and the database to view the location of airsenal home and the current values of all set airsenal environment variables run bash airsenal env get use airsenal env set to set values and store them for future use for example bash airsenal env set k fpl team id v 123456 see airsenal env help for other options getting started if you installed airsenal with conda you should always make sure the airsenalenv virtual environment is activated before running airsenal commands to activate the environment use shell conda activate airsenalenv note most the commands below can be run with the help flag to see additional options and information 1 creating the database once the module has been installed and your team id configured run the following command to create the airsenal database shell airsenal setup initial db this will fill the database with data from the last 3 seasons as well as all available fixtures and results for the current season on linux mac you should get a file tmp data db containing the database on windows you will get a data db file in a the temporary directory returned by the python tempfile module https docs python org 3 library tempfile html on your system you can run sanity checks on the data using the following command shell airsenal check data 2 updating and running predictions to stay up to date in the future you will need to fill three tables match player score and transaction with more recent data using the command shell airsenal update db the next step is to use the team and player level numpyro models to predict the expected points for all players for the next fixtures this is done using the command shell airsenal run prediction weeks ahead 3 we normally look 3 weeks ahead as this is an achievable horizon to run the optimization over but also because things like form and injuries can change a lot in 3 weeks predicted points must be generated before running the transfer or squad optimization see below 3 transfer or squad optimization finally we need to run the optimizer to pick the best transfer strategy over the next weeks and hence the best team for the next week shell airsenal run optimization weeks ahead 3 this will take a while but should eventually provide a printout of the optimal transfer strategy in addition to the teamsheet for the next match including who to make captain and the order of the substitutes you can also optimise chip usage with the arguments wildcard week gw free hit week gw triple captain week gw and bench boost week gw replacing gw with the gameweek you want to play the chip or use 0 to try playing the chip in all gameweeks note that airsenal run optimization should only be used for transfer suggestions after the season has started if it s before the season has started and you want to generate a full squad for gameweek one you should instead use shell airsenal make squad num gameweeks 3 4 apply transfers and lineup to apply the transfers recommended by airsenal to your team on the fpl website run airsenal make transfers this can t be undone you can also use airsenal set lineup to set your starting lineup captaincy choices and substitute order to airsenal s recommendation without making any transfers note that you must have created the fpl login and fpl password files for these to work as described in the configuration section above also note that this command can t currently apply chips such as free hit or wildcard even if those were specified in the airsenal run optimization step if you do want to use this command to apply the transfers anyway you can play the chip at any time before the gameweek deadline via the fpl website run the full airsenal pipeline instead of running the commands above individually you can use shell airsenal run pipeline this will update the database and then run the points predictions and transfer optimization add help to see the available options issues and new features airsenal is regularly developed to fix bugs and add new features if you have any problems during installation or usage please let us know by creating an issue https github com alan turing institute airsenal issues new or have a look through existing issues https github com alan turing institute airsenal issues to see if it s something we re already working on you may also like to try the development version of airsenal which has the latest fixes and features to do this checkout the develop branch of the repo and reinstall shell git pull git checkout develop pip install force reinstall contributing we welcome all types of contribution to airsenal for example questions documentation bug fixes new features and more please see our contributing guidelines contributing md if you re contributing for the first time but not sure what to do a good place to start may be to look at our current issues https github com alan turing institute airsenal issues particularly any with the good first issue tag https github com alan turing institute airsenal issues q is 3aopen is 3aissue label 3a 22good first issue 22 also feel free to just say hello development if you re developing airsenal you may find it helpful to install it in editable mode shell pip install e we re in the process of migrating to poetry https python poetry org docs but as pygmo is not available on pypi on all platforms this is a work on progress however you can set up a development environment without pygmo by running poetry install and then poetry shell to enter the environment we also have a pre commit https pre commit com config to run the code quality tools we use flake8 isort and black automatically when making commits if you re using poetry it will be installed as a dev dependency otherwise run pip install pre commit then to setup the commit hooks shell pre commit install install hooks | hut23 hut23-222 hacktoberfest | ai |
webkitty | logo https raw githubusercontent com yikuansun webkitty master banner svg webkitty web development shouldn t be hard that s why i built webkitty an all in one tool for editing testing and debugging html projects features customizable theme integrated preview google chrome style debugging tools emulation of different hosts and browsers fast file navigation cross platform code suggestions with ctrl space screenshots app screenshot https raw githubusercontent com yikuansun webkitty 30ace57f6263da706dce163de56b8becee2da4a6 screenshots screen 20shot 202022 05 12 20at 209 22 25 20pm png supported operating systems macos 10 11 windows 7 ubuntu 14 04 fedora 28 debian 8 dependencies electron 17 1 0 electron builder 22 14 5 electron remote 2 0 5 adm zip 0 5 5 codemirror 5 65 2 | web ide webdevelopment webdev text-editor html-css-javascript | front_end |
BonoApp.API | backend bonoapp description this repository contains the backend for the bonoapp project team members william s romero moran u201816224 julio alexander salazar zapata u202017572 yordy rolando mochcco atauje u201923959 rocman rody vel squez acosta u20201b553 | server |
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turbopilot | turbopilot turbopilot is deprecated archived as of 30 9 23 there are other mature solutions that meet the community s needs better please read my blog post https brainsteam co uk posts 2023 09 30 turbopilot obit about my decision to down tools and for recommended alternatives mastodon follow https img shields io mastodon follow 000117012 domain https 3a 2f 2ffosstodon org 2f style social https fosstodon org jamesravey bsd licensed https img shields io github license ravenscroftj turbopilot time spent https img shields io endpoint url https wakapi nopro be api compat shields v1 jamesravey all time label 3aturbopilot turbopilot is a self hosted copilot https github com features copilot clone which uses the library behind llama cpp https github com ggerganov llama cpp to run the 6 billion parameter salesforce codegen model https github com salesforce codegen in 4gib of ram it is heavily based and inspired by on the fauxpilot https github com fauxpilot fauxpilot project nb this is a proof of concept right now rather than a stable tool autocompletion is quite slow in this version of the project feel free to play with it but your mileage may vary a screen recording of turbopilot running through fauxpilot plugin assets vscode status gif now supports stablecode 3b instruct https huggingface co stabilityai stablecode instruct alpha 3b simply use thebloke s quantized ggml models https huggingface co thebloke stablecode instruct alpha 3b ggml and set m stablecode new refactored simplified the source code has been improved to make it easier to extend and add new models to turbopilot the system now supports multiple flavours of model new wizardcoder starcoder santacoder support turbopilot now supports state of the art local code completion models which provide more programming languages and fill in the middle support contributing prs to this project and the corresponding ggml fork https github com ravenscroftj ggml are very welcome make a fork make your changes and then open a pr https github com ravenscroftj turbopilot pulls getting started the easiest way to try the project out is to grab the pre processed models and then run the server in docker getting the models you have 2 options for getting the model option a direct download easy quickstart you can download the pre converted pre quantized models from huggingface for low ram users 4 8 gib i recommend stablecode https huggingface co thebloke stablecode instruct alpha 3b ggml and for high power users 16 gib ram discrete gpu or apple silicon i recomnmend wizardcoder https huggingface co thebloke wizardcoder 15b 1 0 ggml resolve main wizardcoder 15b 1 0 ggmlv3 q4 0 bin turbopilot still supports the first generation codegen models from v0 0 5 and earlier builds although old models do need to be requantized you can find a full catalogue of models in models md models md option b convert the models yourself hard more flexible follow this guide https github com ravenscroftj turbopilot wiki converting and quantizing the models if you want to experiment with quantizing the models yourself running turbopilot server download the latest binary https github com ravenscroftj turbopilot releases and extract it to the root project folder if a binary is not provided for your os or you d prefer to build it yourself follow the build instructions build md run bash turbopilot m starcoder f models santacoder q4 0 bin the application should start a server on port 18080 you can change this with the p option but this is the default port that vscode fauxpilot tries to connect to so you probably want to leave this alone unless you are sure you know what you re doing if you have a multi core system you can control how many cpus are used with the t option for example on my amd ryzen 5000 which has 6 cores 12 threads i use bash codegen serve t 6 m starcoder f models santacoder q4 0 bin to run the legacy codegen models just change the model type flag m to codegen instead note turbopilot 0 1 0 and newer re quantize your codegen models old models from v0 0 5 and older i am working on providing updated quantized codegen models running from docker you can also run turbopilot from the pre built docker image supplied here https github com users ravenscroftj packages container package turbopilot you will still need to download the models separately then you can run bash docker run rm it v models models e threads 6 e model type starcoder e model models santacoder q4 0 bin p 18080 18080 ghcr io ravenscroftj turbopilot latest docker and cuda as of release v0 0 5 turbocode now supports cuda inference in order to run the cuda enabled container you will need to have nvidia docker https github com nvidia nvidia docker enabled use the cuda tagged versions and pass in gpus all to docker with access to your gpu like so bash docker run gpus all rm it v models models e threads 6 e model type starcoder e model models santacoder q4 0 bin e gpu layers 32 p 18080 18080 ghcr io ravenscroftj turbopilot v0 2 0 cuda11 7 if you have a big enough gpu then setting gpu layers will allow turbopilot to fully offload computation onto your gpu rather than copying data backwards and forwards dramatically speeding up inference swap ghcr io ravenscroftj turbopilot v0 1 0 cuda11 for ghcr io ravenscroftj turbopilot v0 2 0 cuda12 0 or ghcr io ravenscroftj turbopilot v0 2 0 cuda12 2 if you are using cuda 12 0 or 12 2 respectively you will need cuda 11 or cuda 12 later to run this container you should be able to see app turbopilot listed when you run nvidia smi executable and cuda as of v0 0 5 a cuda version of the linux executable is available it requires that libcublas 11 be installed on the machine i might build ubuntu debs at some point but for now running in docker may be more convenient if you want to use a cuda gpu you can use gpu offloading via the ngl option using the api support for the official copilot plugin support for the official vs code copilot plugin is underway see ticket 11 the api should now be broadly compatible with openai using the api with fauxpilot plugin to use the api from vscode i recommend the vscode fauxpilot plugin once you install it you will need to change a few settings in your settings json file open settings ctrl cmd shift p and select preferences open user settings json add the following values json other settings fauxpilot enabled true fauxpilot server http localhost 18080 v1 engines now you can enable fauxpilot with ctrl shift p and select enable fauxpilot the plugin will send api calls to the running codegen serve process when you make a keystroke it will then wait for each request to complete before sending further requests calling the api directly you can make requests to http localhost 18080 v1 engines codegen completions which will behave just like the same copilot endpoint for example bash curl request post url http localhost 18080 v1 engines codegen completions header content type application json data model codegen prompt def main max tokens 100 should get you something like this json choices logprobs null index 0 finish reason length text n main entry point for this script n logging getlogger setlevel logging info n logging basicconfig format levelname s message s n n parser argparse argumentparser n description doc n formatter class argparse rawdescriptionhelpformatter n epilog doc n created 1681113078 usage total tokens 105 prompt tokens 3 completion tokens 102 object text completion model codegen id 01d7a11b f87c 4261 8c03 8c78cbe4b067 known limitations currently turbopilot only supports one gpu device at a time it will not try to make use of multiple devices acknowledgements this project would not have been possible without georgi gerganov s work on ggml and llama cpp https github com ggerganov ggml it was completely inspired by fauxpilot https github com fauxpilot fauxpilot which i did experiment with for a little while but wanted to try to make the models work without a gpu the frontend of the project is powered by venthe s vscode fauxpilot plugin https github com venthe vscode fauxpilot the project uses the salesforce codegen https github com salesforce codegen models thanks to moyix https huggingface co moyix for his work on converting the salesforce models to run in a gpt j architecture not only does this confer some speed benefits https gist github com moyix 7896575befbe1b99162ccfec8d135566 but it also made it much easier for me to port the models to ggml using the existing gpt j example code https github com ggerganov ggml tree master examples gpt j the model server uses crowcpp https crowcpp org master to serve suggestions check out the original scientific paper https arxiv org pdf 2203 13474 pdf for codegen for more info | code-completion cpp language-model machine-learning | ai |
co-with | co with applications to support students that is infected with the covid 19 demo https youtu be mcbs3u9vaxg | front_end |
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dnx-rtos | dnx rtos the dnx rtos is a general purpose operating system based on the freertos kernel the dnx layer is modeled on well known unix architecture a destination of the system are small microcontrollers supported by the freertos kernel especially 32 bit the system is easy scalable to the user s needs the user can write own drivers virtual devices programs and so on the program layer is mostly compatible with the c standard you can say that the dnx rtos is a freertos kernel distribution the minimal hardware requirements there are minimal hardware requirements that allows to start system but with very minimalistic software base 64 kib flash memory 16 kib ram memory recommended 20 kib the hardware requirements depends on the user s configuration the system features user s terminal via uart interface in example configuration possibility to run many copies of the same program simple porting of pc programs for the dnx due to the c standard compatibility simple drivers layer support many file systems the vfs layer interrupts are not masked by the system the rtos feature dynamic memory allocation simple garbage collector for programs supports many cpu architectures the freertos feature modular design the project configuration to configure the project type in the terminal make config or configure to compile the project type in the terminal make folder structure bsp board support packages configurations build the project s binaries created after compilation config the project s configuration doc documentation res resources included at project build src the project s sources tools the project s tools scripts wizard etc website the whole documenation and exmaples you can found at dnx rtos official website http www dnx rtos org there is also additional software repository with applications https github com devdnl dnx rtos apps | kernel freertos freertos-distribution rtos unix-like command-line stm32f103 ethernet lwip microcontroller lightweight | os |
pompa | pompa fully featured spear phishing toolkit web front end note this is only web frontend for pompa api https github com m1nl pompa api please use pompa docker https github com m1nl pompa docker repository for a full deployment base and read the wiki https github com m1nl pompa wiki getting started prerequisites you will need the following things properly installed on your computer git https git scm com node js https nodejs org with npm ember cli https ember cli com installation git clone https github com m1nl pompa git cd pompa npm install configuration cp config environment js sample config environment js edit config environment js according to your needs running development ember serve visit the app at http localhost 4200 http localhost 4200 building ember build development ember build environment production production further reading useful links pompa api https github com m1nl pompa api pompa docker https github com m1nl pompa docker ember js https emberjs com ember cli https ember cli com license pompa is released under the terms of lgpl 3 0 license author mateusz nalewajski commercial support professional services please contact me directly at mateusz at nalewajski dot pl | front_end |
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Remotely-Controlled-Catapult | remotely controlled catapult the objective of this project is to apply the fundamentals of embedded systems design to remotely control a catapult launching small scale projectiles via an android device the catapult project will fulfill a need to effectively reduce user intervention such that the user won t have to manually retract or lock the catapult arm into place but will only be required to reload the device this is made possible by applying the latest technologies in software development while emphasizing on a networked device materials checklist x arduino uno rev3 x1 x robotgeek sensor shield v2 x1 x fs90mg micro metal gear servos x2 x hc 05 bluetooth transmission module x1 x full sized breadboard x1 x 6v 2a power supply x1 x red female male jumper wires x1 x black female male jumper wires x1 x blue female male jumper wires x1 x green female male jumper wires x1 x 1k ohm watt pth resistor x1 x 2 2k ohm watt carbon film resistor x1 device construction the robotgeek sensor shield is compatible with any arduino board so the sensor shield male header pins should fit perfectly with the female header pins of the arduino uno rev3 just make sure that the male header pins aren t bent when exerting force down on the sensor shield to attach it to the arduino board conveniently the sensor shield converts the arduino s digital io pins into 3 pin adapters so that it is easier to add servos and other sensors to the project under the group of pins labeled voltage in vin attach one servo to dio 10 and the other to dio 11 this is so that the servos draw power from the 6v power supply rather than from the arduino s built in 5v regulator which would limit the servo s range of rotation it is important to make sure that the orange wire on each servo corresponds to the signal s pin the red wire to the voltage v pin and the brown wire to the ground g pin otherwise the current will likely burn out the servos now take the female end of the blue jumper wire and attach it to the signal s pin of dio 12 this will be your rx pin by which you receive serial data from the hc 05 repeat the same for the female end of the green jumper wire on the signal s pin of dio 13 this will be your tx pin by which you transmit serial data to the hc 05 lastly to finish off with the connections to the sensor shield take the female end of the red jumper wire and attach it to the 5v pin and attach the female end of the black jumper wire to the gnd pin the configuration of the sensor shield should look identical to the image provided below sensor shield wiring images wiring schematics jpg raw true robotgeek sensor shield wiring the next step is to attach the male ends of the jumper wires to the breadboard you will need the breadboard the hc 05 bluetooth module the 1k ohm watt resistor and the 2 2k ohm watt resistor specified in the parts list for general assembly the hc 05 bluetooth module has six pins designated for state rx tx gnd vcc and en for this project only rx tx gnd and vcc will be used the bluetooth module has an operating voltage of 5v but only the vcc and gnd pins feature breakout boards to limit the voltage down to 3 3v therefore the 1k and 2k resistor will be used to create a voltage divider as illustrated below to limit the input voltage down to 3 3v for the rx and tx pins so that the bluetooth module is not burnt in the process of connecting it to a power source voltage divider diagram images voltage divider jpg raw true voltage divider for hc 05 start by placing the pins of the hc 05 bluetooth module into the edge of the breadboard parallel to the positive and negative power rails when connecting the male ends of the jumper wires to the breadboard make sure to connect the green jumper wire tx to rx the blue jumper wire rx to tx the black jumper wire gnd to gnd and the red jumper wire 5v to vcc an illustration of these connections is provided below if everything is done correctly a blinking red led will indicate that the hc 05 bluetooth module is powered up and ready to be paired with a client device breadboard wiring images breadboard jpg raw true hc 05 bluetooth module wiring configuring the device to see the device in action simply open up a window in arduino ide and place the arduino code from the src folder into the project solution make sure that the correct microcontroller and port are selected under the tools menu and then proceed to verify upload sketch ino to properly utilize the full potential of the hc 05 bluetooth module within this project one should also download the apk file for the android application from the root of the repo once this has been accomplished step by step instructions to move the apk file from a computer to an android device of choice can be found here at androidbit https www androidpit com android for beginners what is an apk file | os |
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zevision | face recognition and analysis object detection api this project provides an api for face detection sentiment analysis and object detection that can be easily used by developers in custom made applications image https github com zenika open source zevision blob master zevision png the models are based on state of the art computer vision libraries a rest api allows for the training and usage of the models it can run natively on the machine or in a docker container watch a short presentation here https docs google com presentation d e 2pacx 1vsi8fjwrl7eh5fvxtemrhruze f8top4ik5dbx5h bn 3cjx kkxr573 9fv7 20tl p6jtucbcc4 v pub start false loop true delayms 5000 environnement setup installation locally on a ubuntu 16 04 terminal while in the repository s directory launch sudo bash setup sh the installation of all the necessary tools would be underway after you enter your root password support for windows and macos is coming soon using docker in progress library usage guide the library contains the python code used for the model training and prediction library usage and documentation here https github com zenika open source zevision tree master lib api usage guide the api allows access to the different features of the library api usage and documentation here https github com zenika open source zevision tree master api | recognition face-recognition docker-container python flask tensorflow-models dlib opencv opencv-python library server object-detection neural-network deep-learning | ai |
iot-central-firmware | note the code in this repository is provided as an example of how to connect a device to iot central it is not production ready code and is not supported by the azure iot central team azure iot central device platform sample projects reference firmware sample implementation repository for some of the supported devices frameworks are you looking for a particular device sample create an issue on this repository and let us know a quick note about dps endpoint the device provisioning service dps endpoint used in these samples are all coded to work with the dps public cloud endpoint at global azure devices provisioning net for china and other sovereign clouds that do not allow access to this dps public endpoint for these samples to work it is necessary to change this value the correct dps endpoint for the private or sovereign cloud being used contents arduino mkr1000 mkr1010 https github com firedog1024 mkr1000 iotc arduino uno https github com firedog1024 arduino uno wifi iotc azure mxchip iot devkit az3166 mxchip esp32 espressif esp32 esp8266 esp8285 esp8266 freertos b l475e iot01a1 with bg96 cellular freertos b l475e iot01a1 bg96 verizon mbed os mbed os modbus https github com zhaodong2013062 azure iot central modbus gateway http only c httponly csharp http only bash node js httponly bash python client x509 connectivity https github com azure dps certgen tree py sample samples python raspberry pi python raspberrypi micropython 1 9 python 2 7 python 3 4 https pypi org project iotc java 8 with samples https github com lucadruda iotc java device client node js with samples https github com lucadruda iotc nodejs device client device provisioning helper tools x509 helper tool https github com azure dps certgen symmetric key helper tool https github com azure dps keygen reporting security issues security issues and bugs should be reported privately via email to the microsoft security response center msrc at secure microsoft com mailto secure microsoft com you should receive a response within 24 hours if for some reason you do not please follow up via email to ensure we received your original message further information including the msrc pgp https technet microsoft com en us security dn606155 key can be found in the security techcenter https technet microsoft com en us security default contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g label comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments | server |
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paefax-language-backend | paefax language quiz backend lines https img shields io tokei lines github paefax paefax language backend pulls https img shields io github issues pr closed paefax paefax language backend this app is part of our final project in our agile course and web application course at it h gskolan in gothenburg the purpose is to create a complete fullstack app this being the backend for some sort of quiz focused on learning using the agile software development approach and scrum we decided to make a language learning app taking inspiration from apps like duolingo and the like we use trello a scrum master and development team with our teacher kevin as our customer and product owner trying our best to follow a professional and structured approach of an agile team with regular sprints one week sprints daily stand ups reviews and retrospectives every end of sprint project setup clone this repository sh https github com paefax paefax language backend git env file change change generate private key to a generated private key from autho https auth0 com sh access token secret generate private key install dependencies in local repository sh npm install start application sh node app js endpoints run the backend and browse to localhost 3000 to access the endpoints http verb url authorization info get none returns a static list of five fruits for testing get fruit none returns five random words from the fruit database get endpoints are used in the frontend webapp linked below useful links agile board https trello com b ygds6lc3 where we have our user stories backlog current sprint log and where we plan and execute our work lofi wireframe https www figma com file bulvwva1zg2pkhogge8ujg quiz app node id 13 3a2 our first draft for the look of the frontend with basic functionality hifi wireframe https www figma com file qofogdtr8pti5widognkvh language quiz hifi wireframe node id 24 3a71 our lofi with added color theme and fonts frontend repository https github com paefax paefax language app our frontend repository project members philippe vial https github com philippevial felix jacobsen https github com felixjacobsen fredrik eriksson https github com erikssonf helena eklund https github com helenahalldiniths patrik fallqvist magnusson https github com lordrekishi technologies ide https img shields io badge visual studio 5c2d91 style for the badge logo visual 20studio logocolor white trello https img shields io badge trello 0052cc style for the badge logo trello logocolor white figma https img shields io badge figma f24e1e style for the badge logo figma logocolor white vue https img shields io badge vue js 35495e style for the badge logo vue js logocolor 4fc08d node https img shields io badge node js 43853d style for the badge logo node js logocolor white exp https img shields io badge express js 404d59 style for the badge sql https img shields io badge sqlite 07405e style for the badge logo sqlite logocolor white | agile-development express figma nodejs scrum trello vscode vue vuejs | server |
rpi-iot | rpi iot iot project for embedded system design for raspberry pi april 2023 david andrino and fernando sanz subprojects iotclient udp client hosted in the raspberry pi that takes measurements and sends it via udp ip to the server iotclient udp server in linux that receives measurements via udp ip from the client thingsboard client client for the raspberry pi that takes measurements and sends it to the thingsboard server via a mosquito client sources iotclient server basic implementation https www educative io answers how to implement udp sockets in c raspberryclient cjson library https github com davegamble cjson raspberryserver basic node js server https bipinparajuli com np blog create udp clinet and server with node js thingsboard client thingsboard installation https thingsboard io docs user guide install ubuntu getting started with thingsboard https thingsboard io docs getting started guides helloworld connectdevice mqtt linux | os |
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---Databases-2019 | databases 2019 a repo containing the databases project that was part of the databases course in the department of electrical amp computer engineering of aristotle university of thessaloniki | server |
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jspp | js jspp official website https www onux com jspp please visit the official website to download the js compiler and stay up to date | language languages programming-language programming-languages javascript nodejs node-js js jspp compiler | front_end |
poseidon | h1 align center br a href https karthithehacker com poseidon img src https github com karthi the hacker poseidon raw main webpublic images poseidon png width 400px alt poseidon a h1 h2 align center a cloud based tool for social engineering attacks and security audit h2 screen shots h1 align center h2 align center dashboard h2 h1 align center img align center src https github com karthi the hacker poseidon raw main webpublic images screenshots 1 png width 700px alt poseidon h1 h2 align center attack menu h2 h1 align center img align center src https github com karthi the hacker poseidon raw main webpublic images screenshots 2 png width 700px alt poseidon h1 h2 align center attack menu h2 h1 align center img align center src https github com karthi the hacker poseidon raw main webpublic images screenshots 3 png width 700px alt poseidon h1 h2 align center pwned data h2 h1 align center img align center src https github com karthi the hacker poseidon raw main webpublic images screenshots 4 png width 700px alt poseidon h1 h1 templates facebook instagram google linkedin adobe paypal badoo netflix pinterest playstation pornhub protonmail twitch twitter wordpress yahoo gitlab github requirements php https php net node js https nodejs org installation 1 install nodejs instructions here https nodejs org en download package manager if you can t figure this out you shouldn t really be using this 2 install pm2 npm install pm2 g 3 download and extract the latest release from here https github com karthi the hacker poseidon git 4 in the extracted folder run these commands npm install install dependencies pm2 start index js start the script pm2 startup to run poseidon on startup 6 set a username 1 stop poseidon pm2 stop index js 2 open maindb json in a text editor 3 under admin set the username as plain text 4 save the file 5 run pm2 restart all 7 unzip webpage zip unzip webpages zip unzip 8 in your browser navigate to http server ip 443 company profile a href https university cappriciosec com img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 1 png width 200px a a href https www cappriciosec com img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 2 png width 200px a a href https www instagram com cappriciosec img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 6 png width 200px a contact info a href https karthithehacker com img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 3 png width 200px a a href https www youtube com karthithehacker img src https github com karthi the hacker gh0str3c0n raw main screenshots banner y png width 200px a a href https www instagram com karthithehacker img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 5 png width 200px a a href https www linkedin com in cyberspartan img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 7 png width 200px a a href https api whatsapp com send phone 918270913635 text hi img src https github com karthi the hacker gh0str3c0n raw main screenshots banner 8 png width 200px a | cloud |
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html-starter-bs4-webpack | img src http marcin silversite pl html starter bs4 webpack logo small png width 150 height 58 alt html starter bootstrap https flat badgen net badge bootstrap 4 3 7952b3 https getbootstrap com webpack https flat badgen net badge webpack 4 14aaf3 https webpack js org dependencies https flat badgen net david dep cichy380 html starter bs4 webpack https david dm org cichy380 html starter bs4 webpack devdependencies https flat badgen net david dev cichy380 html starter bs4 webpack https david dm org cichy380 html starter bs4 webpack type dev license https flat badgen net github license cichy380 html starter bs4 webpack https github com cichy380 html starter bs4 webpack blob master license md kick start your project with bootstrap https getbootstrap com the world s most popular framework and modern development workflow this boilerplate with webpack https webpack js org based setup helps you build web apps and sites much faster features live reloading br browser update after changes automatically optimizes entry files br concatenate minify and inject output files to html sass https sass lang com for stylesheets br with the 7 1 pattern https sass guidelin es the 7 1 pattern modern javascript br es6 modules based code linting by eslint https eslint org older browsers support add vendor prefixes in css with autoprefixer https autoprefixer github io convert es6 code into a backwards compatible with babel https babeljs io includes webpack 4 https webpack js org configuration module bundler bootstrap 4 http getbootstrap com the most popular html css and js framework jquery http jquery com javascript library font awesome 5 https fontawesome com the web s most popular vector icons and social logos google fonts https fonts google com libre licensed fonts sourcemaps and more theme development node js http nodejs org is the only required dependency to work with html starter installation 1 install node js http nodejs org installation depends on your system after finishing you will be able to check the version number using node v and npm v commands npm is distributed with node js https www npmjs com get npm 2 clone the repo using git clone https github com cichy380 html starter bs4 webpack git or download html starter https github com cichy380 html starter bs4 webpack archive master zip 3 open folder html starter bs4 webpack command cd html starter bs4 webpack and install a packages of html starter via npm install command now you have everything you need to run the build process build commands npm run start compile assets when file changes are made start webpack dev server https github com webpack webpack dev server session npm run build compile and optimize the files in your assets directory for production structure shorten directories and files structure which you ll see after build shell html starter bs4 webpack assets template assets fonts place template fonts files here html template html files partials common parts of html code 404 html placeholder 404 error page index html default html skeleton images template images files scripts template javascript files vendor necessary parts of frameworks and libs bootstrap fontawesome jquery main js main javascript file that references js source files scss template styles 7 1 sass architecture folders main scss main sass file that references scss source files index js entry point of template miscellaneous dist output folder with production build don t edit css output styles images output images js output javascripts index html homepage miscellaneous node modules node js packages don t edit babelrc babel configuration file eslintrc js eslint configuration file package json node js dependencies and scripts webpack config js webpack configuration file package lock json node js dependencies lock file don t edit other demo sample of html starter usage placed in separate branche demo branch https github com cichy380 html starter bs4 webpack tree demo simple corporate website license code released under the mit license https github com cichy380 html starter bs4 webpack blob master license md | bootstrap webpack sass | front_end |
azure-iot-samples-node | exclamation archived warning all the samples have been moved to our main repository azure iot sdk node https github com azure azure iot sdk node please submit an issue or pr in the azure iot sdk node https github com azure azure iot sdk node repository if there are additional samples you would like to see azure iot samples for node js azure iot samples node provides a set of easy to understand continuously tested samples for connecting to azure iot hub via azure azure iot sdk node prerequisites node js lts or current version on your development machine you can download node js for multiple platforms from nodejs org https nodejs org you can verify the current version of node js on your development machine using node version resources azure iot sdk node https github com azure azure iot sdk node contains the source code for azure iot node sdk azure iot hub documentation https docs microsoft com azure iot hub | server |
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nano-server | nano server nano server is an ultra lightweight node js http server for web development it exposes a local directory through http simplifying development of front end apps that require functionality blocked by cross origin protections e g ajax https developer mozilla org en docs ajax canvas image processing http www w3schools com tags canvas getimagedata asp web audio analysis https developer mozilla org en us docs web api web audio api webgl textures https developer mozilla org en us docs web webgl cross domain textures nano server provides some features that i didn t find in similar server packages sends gzipped versions of files if they are available attempts to set the content type header with the appropriate mime type for browser relevant file types 0 dependencies can be dropped into any project usage the simplest way to install nano server is as a global install through npm bash npm install g nano server then nano server can be invoked as a command line executable bash nano server by default nano server will run on port 5000 and use the current working directory as its document root optionally a port number can be passed as first argument on the command line bash nano server 3000 the document root can be given as second argument if desired bash nano server 5000 my app public html since nano server has no dependancies it can be dropped directly into any project and invoked using node explicitly this can be useful if you don t have the necessary permissions to do a global install on the machine you re using bash cp nano server path to project cd path to project node nano server | front_end |
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covid-care-website | covid care website language used php 7 br database mysql 8 0 br user interface design html css ajax javascript br software xampp wamp mamp lamp anyone br project features br a site which provides information regarding the availability of beds medicines in nearby hospital medicals respectively along with covid stats of different countries technology used php js mysql api html css bootstrap br br admin module br user module br admin module br login this module is used for admin login br dashboard br manage hospital medical admin can add and manage details add edit and delete br moderator registration admin can register new moderators for the website br change password br logout br br user module br hospital medical user can search for hospitals medicals br covid stats user can check daily stats related to covid cases br contact user can contact for any issue query suggestion br br software requirement any one br wamp br xampp br mamp br lamp br steps for software installation br wamp https youtu be xszapw kyhq br xampp https youtu be tdizwoiewk br mamp https youtu be tb7kteydhre br lamp https www youtube com watch v zwtrnxmtvym br installation br open phpmyadmin br create a database admin panel br import database admin panel sql br open http localhost covid care website admin panel login php using any browser br login details br login details for admin 1 username admin covidcare com br 2 password 1234 br create another database covid care for hospital medical contact page br import database covid care sql br br br done | server |
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Statistics-for-Machine-Learning | statistics for machine learning this is the code repository for statistics for machine learning https www packtpub com big data and business intelligence statistics machine learning utm source github utm medium repository utm campaign 9781788295758 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book complex statistics in machine learning worry a lot of developers knowing statistics helps you build strong machine learning models that are optimized for a given problem statement this book will teach you all it takes to perform complex statistical computations required for machine learning you will gain information on statistics behind supervised learning unsupervised learning reinforcement learning and more you will see real world examples that discuss the statistical side of machine learning and familiarize yourself with it you will come across programs for performing tasks such as model parameter fitting regression classification density collection working with vectors matrices and more by the end of the book you will have mastered the required statistics for machine learning and will be able to apply your new skills to any sort of industry problem instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 the code will look like the following import numpy as np from scipy import stats data np array 4 5 1 2 7 2 6 9 3 calculate mean dt mean np mean data print mean round dt mean 2 calculate median dt median np median data print median dt median calculate mode dt mode stats mode data print mode dt mode 0 0 this book assumes that you know the basics of python and r and how to install the libraries it does not assume that you are already equipped with the knowledge of advanced statistics and mathematics like linear algebra and so on the following versions of software are used throughout this book but it should run fine with any more recent ones as well anaconda 3 4 3 1 all python and its relevant packages are included in anaconda python 3 6 1 numpy 1 12 1 pandas 0 19 2 and scikit learn 0 18 1 r 3 4 0 and rstudio 1 0 143 theano 0 9 0 keras 2 0 2 related products machine learning for developers https www packtpub com big data and business intelligence machine learning developers utm source github utm medium repository utm campaign 9781786469878 scala for machine learning https www packtpub com big data and business intelligence scala machine learning utm source github utm medium repository utm campaign 9781783558742 machine learning for opencv https www packtpub com big data and business intelligence machine learning opencv utm source github utm medium repository utm campaign 9781783980284 download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781788295758 https packt link free ebook 9781788295758 a p | ai |
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serverless | the power of serverless for front end web developers feel free to contribute the content is mostly markdown files bopped into the appropriate folders site is built on astro https astro build you should be able to pull it npm install and npm run dev and it ll come up | front_end |
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Ping-Pong-Game-with-FSM-Datapath-in-Logisim | ping pong game with fsm logisim in logisim design a 2d ping pong game you must implement your circuit as a finite state machine with datapath using joystick gets extra points no fsm no grade c code state diagram state transition table boolean expressions must be included in a report demo is a must cheating is at least punished by 100 it is not a group project | os |
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Blockchain-Developer-Resources | blockchain developer resources a list of opininated links to resources useful to blockchain and bitcoin developers bitcoin non technical introductions non technical videos very intro bitcoin explained and made simple https www youtube com watch v s4g1xfu8gto 3m 24s by the guardian http www theguardian com the essence of how bitcoin works non technical https www youtube com watch v t5jgqxcte3c 5m 24s by curious inventor http patreon com curiousinventor non technical videos more detail blockchain university bitcoin the blockchain an introduction https www youtube com watch v zuoxuw9zvms 26m 16s by christophera https twitter com christophera bitcoin 101 what is bitcoin https www youtube com watch v bhe61janflu 22m 32s by james d angelo wbn https www youtube com channel ucgo7fccpuylvk4lup3jagvw non technical videos intermediate related topics blockchain university bitcoin keys addresses and wallets https www youtube com watch v ic76isncb 0 1h 06m 43s by christophera https twitter com christophera bitcoin sidechains https www youtube com watch v 6sxnvianl2y 5m 54s by diginomics https diginomics com non technical articles what is bitcoin http www coindesk com information what is bitcoin by coindesk what is bitcoin http money cnn com infographic technology what is bitcoin by cnn money the impact of the blockchain goes beyond financial services https hbr org 2016 05 the impact of the blockchain goes beyond financial services by don an alex tapscott technical introductions technical videos how bitcoin works in 5 minutes technical https www youtube com watch v l9jojk30eqs 5m 25s by curious inventor http patreon com curiousinventor how bitcoin works under the hood https www youtube com watch v lx9zgzcmqxe 22m 24s by curious inventor http patreon com curiousinventor mechanics of bitcoin https www youtube com watch v t3hjsfppmxs 1h 19m 49s by princeton bitcoin and cryptocurrency technologies online course https www coursera org course bitcointech programming bitcoin youtube channel https www youtube com programmingbitcoin by murray coding multi signature addresses https www youtube com watch v zibusazbjgu by d angelo technical books mastering bitcoin https github com aantonop bitcoinbook by andreas m antonopoulos llc technical articles elliptic curve digital signature algorithm and its applications in bitcoin http cs ucsb edu koc ecc project 2015projects malvik witzoee pdf by arnt gunnar malvik and bendik witzoee programming bitcoin transaction scripts https docs google com document d 1d gi 7sf9soyahg25cmpoo4xtlq3ijutjrwczxflv1e edit by kofler developer s introduction to bitcoin http bitcoinmagazine com 9249 developers introduction bitcoin by buterin how bitcoin works under the hood http www imponderablethings com 2013 07 how bitcoin works under hood html by driscoll bitcoins the hard way using the raw bitcoin protocol http www righto com 2014 02 bitcoins hard way using raw bitcoin html by shirriff bitcoin mining the hard way the algorithms protocols and bytes http www righto com 2014 02 bitcoin mining hard way algorithms html by shirriff tutorials cryptographic currencies crash course c4 http www2016 net proceedings companion p1021 pdf by aljosha judmayer and edgar weipp the libbitcoin tutorial http libbitcoin dyne org doc by taaki how to parse the bitcoin blockchain http codesuppository blogspot com 2014 01 how to parse bitcoin blockchain html by ratcliff signing offline transactions https gist github com jashmenn 9811205 by maxwell 2 of 2 escrow example https gist github com jashmenn 9811198 by maxwell 2 of 3 multisig example https gist github com jashmenn 9811185 by andresen how to decrypt messages in the blockchain from btcmsg https gist github com ripper234 1625828 by ripper234 sidechains sidechains are new blockchains but are backed by bitcoin rather than being an altcoin using two way pegging these sidechains provide a method for developers to make changes and play around with blockchain rules in a separate blockchain while keeping these coins linked to bitcoin introduction ask dr bitcoin what are side chains http siliconangle com blog 2014 04 21 bitcoin sidechains introduction to sidechains and blockchain 2 0 https www deepdotweb com 2014 06 26 sidechains blockchain 2 0 sidechains treechains the tl dr http blog greenaddress it 2014 06 13 sidechains treechains the tldr by sanders side chains the how the challenges and the potential http bitcoinmagazine com 12349 side chains challenges potential by buterin sidechain technical feasibility discussion https bitcointalk org index php topic 566704 0 all discussion alternative chains merged mining https en bitcoin it wiki alternative chains concepts the initial two way pegging proposal http sourceforge net p bitcoin mailman message 32108143 by adam back blockstream whitepaper enabling blockchain innovations with pegged sidechains https www blockstream com sidechains pdf tree chains preliminary summary http www mail archive com bitcoin development lists sourceforge net msg04388 html by todd tutorials sidechains alpha sidechain tutorial http blog cryptoiq ca p 395 projects sidechain elements project https github com elementsproject elementsproject github io open source github project investigating and experiment with various bitcoin sidechain concepts altcoins introduction a treatise on altcoins https download wpsoftware net bitcoin alts pdf tutorials how to clone scrypt based altcoins for fun and profit http devtome com doku php id scrypt altcoin cloning guide rev 1391981820 by shakezula projects coingecko https www coingecko com en has charts for a number of altcoins but more importantly attempts to measure developer and user community activity javascript bitcoin at blockchain university we use a number of javascript based examples to teach the more technical details of bitcoin you are not required to have an in depth knowledge of javascript but learning some basics is very useful setting up on mac if using a mac you ll need some basic knowledge how to use the terminal and the mac s command line interface and you ll need to install brew node and git a basic tutorial on how to do this is at https github com christophera intro mac command line you can also use this script which sets up your mac automatically but the above teaches you how do to do it manually https github com blockchainu prepare osx for blockchain webdev introduction to javascript javascript is in both server node and client browser development some basics of javascript are common to both here are some resources for learning about javascript that are generally applicable to both platform learn javascript https www gitbook com book gitbookio javascript details a free online book with interactive exercises introduction to node javascript server based javascript typically uses node these javascript learning resources are node specific i like the command line based nodeschool io http nodeschool io tutorials as they require you to both use the command line and to create real working code these are the basic interactive tutorials but there are many more available learn javascripting basics npm install g javascripting learn node basics npm install g learnyounode learn git npm install g git it online courses on javascript most of these courses teach general javascript but tend to be more client side javascript oriented free online courses code academy javascript http www codecademy com tracks javascript is an online interactive course that comes highly recommend the course says 10 hours but one of our students reported that it took him about 17 hours over 5 days and found q a forum and glossary both helpful how to learn javascript properly http javascriptissexy com how to learn javascript properly introduction to computing principles http web stanford edu class cs101 introduction to javascript development https www udemy com refactoru intro js dtcode dvgkz5c30m1i some non free online courses node js essential training http www lynda com javascript tutorials nodejs essential training 141132 2 html general javascript books and ebooks some general javascript online books in ebook in rough order of preference eloquent javascript http eloquentjavascript net javascript guide https developer mozilla org en us docs web javascript guide javascript garden http bonsaiden github io javascript garden local client javascript tools playgrounds these tools can be cloned from github to allow you to use your browser to play around with various bitcoin capabilities bip32 generator https github com bip32 bip32 github io git clone https github com bip32 bip32 github io git cd bip32 github io open index html lets you create bip32 deterministic heirarchical keys prefix xpub and xprv based on a simple brainwallet aka arbitrary mnemonic passphrase bip39 mnemonic code converter https github com dcpos bip39 npm install bip39 npm run compile lets you create bip39 mnemonics typically 12 words used for deterministic keys typically for bip32 bitcore js bitcoin 101 getting started with bitcore a full javascript implementation of bitcoin https www youtube com watch v tmkn8yyyov8 https github com wobine blackboard101 blob master bitcoredayone html code for the above bitcoin quick tools https github com chulini bitcoin quick tools simple browser based app loaded locally that is bitcore js has key and address generator and simple transaction tool useful working example of browserfy bitcore js code testnet faucets you ll need bitcoin testnet coins while developing apps with bitcoin list in rough order of reliability and number of coins offered sidechains elements project testnet faucet https testnet faucet elementsproject org 10 btc per day per ip tp faucet http tpfaucet appspot com 1 9 btc per request bitcoin standards bip32 bitcoin deterministic heirarchical keys good article on deterministic wallets http blog richardkiss com p 313 discussion of bip32 advantages and flaws https bitcoinmagazine com 8396 deterministic wallets advantages flaw the bip0032 standard https en bitcoin it wiki bip 0032 bip32 generator https github com bip32 bip32 github io git clone https github com bip32 bip32 github io git cd bip32 github io open index html lets you create bip32 deterministic heirarchical keys prefix xpub and xprv based on a simple brainwallet aka arbitrary mnemonic passphrase blockstack keychains js https github com blockstack blockstack keychains js blockchain explorers blockr io http btc blockr io blockchain info https blockchain info | blockchain |
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financial-platform-with-blockchain | financial platform with blockchain all all | blockchain |
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heroes-service | tutorial hyperledger fabric sdk go how to build your first app source chainhero io 2018 06 tutorial build blockchain app v1 1 0 https chainhero io 2018 06 tutorial build blockchain app v1 1 0 this tutorial will introduce you to the hyperledger fabric go sdk and allows you to build a simple application using the blockchain principle this tutorial uses hyperledger fabric version 1 1 0 this is the first part of this tutorial the basics sdk features will be shown but the second part is scheduled to demonstrate a more complex application 1 prerequisites this tutorial won t explain in detail how hyperledger fabric works i will just give some tips to understand the general behavior of the framework if you want to get a full explanation of the tool go to the official documentation http hyperledger fabric readthedocs io en latest there is a lot of work there that explains what kind of blockchain hyperledger fabric is this tutorial has been made on ubuntu 16 04 but the hyperledger fabric framework is compatible with mac os x windows and other linux distributions we will use the go language to design our first application because the hyperledger fabric has been also built in go and the fabric sdk go is really simple to use in addition the chaincode smart contract can be written in go too so the full stack will be only in go awesome right however if you are go phobic there are other sdk like for nodejs java or python but we won t discuss about them here hyperledger fabric uses docker to easily deploy a blockchain network in addition some components peers also deploys docker containers to separate data channel so make sure that your platform supports this kind of virtualization 2 introduction to hyperledger fabric hyperledger fabric is a platform for distributed ledger solutions underpinned by a modular architecture delivering high degrees of confidentiality resiliency flexibility and scalability it is designed to support pluggable implementations of different components and accommodate the complexity and intricacies that exist across the economic ecosystem see the full explanation from the official documentation in the introduction part hyperledger fabric blockchain http hyperledger fabric readthedocs io en latest blockchain html blockchain concensus http hyperledger fabric readthedocs io en latest images consensus png 3 installation guide this tutorial was made on ubuntu 16 04 but there is help for windows mac os x and other linux distributions users a docker docker version 17 03 0 ce or greater is required linux ubuntu first of all in order to install docker correctly we need to install its dependencies bash sudo apt install apt transport https ca certificates curl software properties common once the dependencies are installed we can install docker bash curl fssl https download docker com linux ubuntu gpg sudo apt key add sudo add apt repository deb arch amd64 https download docker com linux ubuntu lsb release cs stable sudo apt update sudo apt install y docker ce now we need to manage the current user to avoid using administration rights root access when we will use the docker command to do so we need to add the current user to the docker group bash sudo groupadd docker sudo gpasswd a user docker sudo service docker restart do not mind if groupadd group docker already exists error pop up to apply the changes made you need to logout login you can then check your version with bash docker v end of the docker installation docs images finish docker install png mac os x download and install the latest docker dmg https docs docker com docker for mac install package for mac os x available on the docker https docs docker com docker for mac install website this will install docker compose as well so you can skip the next step linux not ubuntu see links below debian https docs docker com engine installation linux docker ce debian fedora https docs docker com engine installation linux docker ce fedora centos https docs docker com engine installation linux docker ce centos windows see instructions from the docker website docker com docker for windows https docs docker com docker for windows install b docker compose docker compose version 1 8 or greater is required we are currently unable to manage easily multiple containers at once to solve this issue we need docker compose linux the installation is pretty fast bash sudo curl l https github com docker compose releases download 1 21 2 docker compose uname s uname m o usr local bin docker compose sudo chmod x usr local bin docker compose apply these changes by logout login and then check its version with bash docker compose version end of the docker compose installation docs images finish docker compose install png windows others see instructions from the docker compose website docs docker com compose install https docs docker com compose install c go go version 1 9 x or greater is required linux you can either follow instructions from golang org https golang org dl or use those generics commands that will install golang 1 9 2 and prepare your environment generate your gopath for ubuntu bash wget https storage googleapis com golang go1 9 2 linux amd64 tar gz sudo tar c usr local xzf go1 9 2 linux amd64 tar gz rm go1 9 2 linux amd64 tar gz echo export path path usr local go bin sudo tee a etc profile echo export gopath home go tee a home bashrc echo export path path goroot bin gopath bin tee a home bashrc mkdir p home go src pkg bin to make sure that the installation works you can logout login again and run bash go version end of the go installation docs images finish go install png windows mac os x others see instructions from the golang website golang org install https golang org doc install 4 make your first blockchain network a prepare environment in order to make a blockchain network we will use docker to build virtual computers that will handle different roles in this tutorial we will stay as simple as possible hyperledger fabric needs a lot of certificates to ensure encryption during the whole end to end process tsl authentications signing blocks the creation of these files requires a little time and in order to go straight to the heart of the matter we have already prepared all this for you in the folder fixtures in this repository make a new directory in the src folder of your gopath following our repository naming bash mkdir p gopath src github com chainhero heroes service cd gopath src github com chainhero heroes service to get the fixtures folder you can either follow this command line which will install and use subversion to get the folder from this repository or download the zip file from github https github com chainhero heroes service archive v1 1 0 zip and extract only the fixtures folder bash sudo apt install y subversion cd gopath src github com chainhero heroes service svn checkout https github com chainhero heroes service branches v1 1 0 fixtures rm rf fixtures svn alternatively if you want to know how to build this fixture folder and learn how to create the blockchain network follow this quick tutorial on how to build your first network https chainhero io 2018 04 tutorial hyperledger fabric how to build your first network b test in order to check if the network works we will use docker compose to start or stop all containers at the same time go inside the fixtures folder and run bash cd gopath src github com chainhero heroes service fixtures docker compose up you will see a lot of logs with different colors for your information red isn t equal to errors open a new terminal and run bash docker ps docker compose up screenshot docs images docker ps png you will see two peers the orderer and one ca containers you have successfully made a new network ready to use with the sdk to stop the network go back to the previous terminal press ctrl c and wait that all containers are stopped if you want to explore more deeper follow our tutorial dedicated to the network part here https chainhero io 2018 04 tutorial hyperledger fabric how to build your first network or check out the official documentation about this building your first network http hyperledger fabric readthedocs io en latest build network html tips when the network is stopped all containers used remain accessible this is very useful to check logs for example you can see stopped containers with docker ps a in order to clean up these containers you need to delete them with docker rm docker ps aq or if you have used a docker compose file go where this file is and run docker compose down tips you can run the docker compose command in background to keep the prompt to do so use the parameter d like this docker compose up d to stop containers place yourself in the same folder where the docker compose yaml is and run docker compose stop or docker compose down to additionally clean up all containers when they are stopped 5 use the fabric sdk go a configuration our application needs a lot of parameters especially the addresses of the fabric s components to communicate we will put everything in a new configuration file the fabric sdk go configuration and our custom parameters for the moment we will only try to make the fabric sdk go works with the default chaincode bash cd gopath src github com chainhero heroes service vi config yaml yaml name heroes service network schema version of the content used by the sdk to apply the corresponding parsing rules version 1 0 0 the client section used by go sdk client which organization does this application instance belong to the value must be the name of an org defined under organizations organization org1 logging level info global configuration for peer event service and orderer timeouts if this this section is omitted then default values will be used same values as below peer timeout connection 10s response 180s discovery expiry period for discovery service greylist filter the channel client will greylist peers that are found to be offline to prevent re selecting them in subsequent retries this interval will define how long a peer is greylisted greylistexpiry 10s eventservice event service type optional if not specified then the type is automatically determined from channel capabilities type deliver eventhub the below timeouts are commented out to use the default values that are found in pkg fab endpointconfig go the client is free to override the default values by uncommenting and resetting the values as they see fit in their config file timeout connection 15s registrationresponse 15s orderer timeout connection 15s response 15s global timeout query 180s execute 180s resmgmt 180s cache connectionidle 30s eventserviceidle 2m channelconfig 30m channelmembership 30s discovery 10s selection 10m root of the msp directories with keys and certs cryptoconfig path gopath src github com chainhero heroes service fixtures crypto config some sdks support pluggable kv stores the properties under credentialstore are implementation specific credentialstore path tmp heroes service store optional specific to the cryptosuite implementation used by go sdk software based implementations requiring a key store pkcs 11 based implementations does not cryptostore path tmp heroes service msp bccsp config for the client used by go sdk bccsp security enabled true default provider sw hashalgorithm sha2 softverify true level 256 tlscerts optional use system certificate pool when connecting to peers orderers for negotiating tls default false systemcertpool false optional client key and cert for tls handshake with peers and orderers client keyfile certfile optional but most apps would have this section so that channel objects can be constructed based on the content below if an app is creating channels then it likely will not need this section channels name of the channel chainhero required list of orderers designated by the application to use for transactions on this channel this list can be a result of access control org1 can only access orderera or operational decisions to share loads from applications among the orderers the values must be names of orgs defined under organizations peers deprecated not recommended to override any orderer configuration items entity matchers should be used orderers orderer example com required list of peers from participating orgs peers peer0 org1 hf chainhero io optional will this peer be sent transaction proposals for endorsement the peer must have the chaincode installed the app can also use this property to decide which peers to send the chaincode install request default true endorsingpeer true optional will this peer be sent query proposals the peer must have the chaincode installed the app can also use this property to decide which peers to send the chaincode install request default true chaincodequery true optional will this peer be sent query proposals that do not require chaincodes like queryblock querytransaction etc default true ledgerquery true optional will this peer be the target of the sdk s listener registration all peers can produce events but the app typically only needs to connect to one to listen to events default true eventsource true peer1 org1 hf chainhero io policies optional options for retrieving channel configuration blocks querychannelconfig optional min number of success responses from targets peers minresponses 1 optional channel config will be retrieved for these number of random targets maxtargets 1 optional retry options for query config block retryopts optional number of retry attempts attempts 5 optional the back off interval for the first retry attempt initialbackoff 500ms optional the maximum back off interval for any retry attempt maxbackoff 5s optional he factor by which the initial back off period is exponentially incremented backofffactor 2 0 list of participating organizations in this network organizations org1 mspid org1 hf chainhero io cryptopath peerorganizations org1 hf chainhero io users username org1 hf chainhero io msp peers peer0 org1 hf chainhero io peer1 org1 hf chainhero io optional certificate authorities issue certificates for identification purposes in a fabric based network typically certificates provisioning is done in a separate process outside of the runtime network fabric ca is a special certificate authority that provides a rest apis for dynamic certificate management enroll revoke re enroll the following section is only for fabric ca servers certificateauthorities ca org1 hf chainhero io list of orderers to send transaction and channel create update requests to for the time being only one orderer is needed if more than one is defined which one get used by the sdk is implementation specific consult each sdk s documentation for its handling of orderers orderers orderer hf chainhero io url localhost 7050 these are standard properties defined by the grpc library they will be passed in as is to grpc client constructor grpcoptions ssl target name override orderer hf chainhero io these parameters should be set in coordination with the keepalive policy on the server as incompatible settings can result in closing of connection when duration of the keep alive time is set to 0 or less the keep alive client parameters are disabled keep alive time 0s keep alive timeout 20s keep alive permit false fail fast false allow insecure will be taken into consideration if address has no protocol defined if true then grpc or else grpcs allow insecure false tlscacerts certificate location absolute path path gopath src github com chainhero heroes service fixtures crypto config ordererorganizations hf chainhero io tlsca tlsca hf chainhero io cert pem list of peers to send various requests to including endorsement query and event listener registration peers peer0 org1 hf chainhero io this url is used to send endorsement and query requests url localhost 7051 eventurl is only needed when using eventhub default is delivery service eventurl localhost 7053 grpcoptions ssl target name override peer0 org1 hf chainhero io these parameters should be set in coordination with the keepalive policy on the server as incompatible settings can result in closing of connection when duration of the keep alive time is set to 0 or less the keep alive client parameters are disabled keep alive time 0s keep alive timeout 20s keep alive permit false fail fast false allow insecure will be taken into consideration if address has no protocol defined if true then grpc or else grpcs allow insecure false tlscacerts certificate location absolute path path gopath src github com chainhero heroes service fixtures crypto config peerorganizations org1 hf chainhero io tlsca tlsca org1 hf chainhero io cert pem peer1 org1 hf chainhero io this url is used to send endorsement and query requests url localhost 8051 eventurl is only needed when using eventhub default is delivery service eventurl localhost 8053 grpcoptions ssl target name override peer1 org1 hf chainhero io these parameters should be set in coordination with the keepalive policy on the server as incompatible settings can result in closing of connection when duration of the keep alive time is set to 0 or less the keep alive client parameters are disabled keep alive time 0s keep alive timeout 20s keep alive permit false fail fast false allow insecure will be taken into consideration if address has no protocol defined if true then grpc or else grpcs allow insecure false tlscacerts certificate location absolute path path gopath src github com chainhero heroes service fixtures crypto config peerorganizations org1 hf chainhero io tlsca tlsca org1 hf chainhero io cert pem fabric ca is a special kind of certificate authority provided by hyperledger fabric which allows certificate management to be done via rest apis application may choose to use a standard certificate authority instead of fabric ca in which case this section would not be specified certificateauthorities ca org1 hf chainhero io url http localhost 7054 fabric ca supports dynamic user enrollment via rest apis a root user a k a registrar is needed to enroll and invoke new users httpoptions verify false registrar enrollid admin enrollsecret adminpw optional the optional name of the ca caname ca org1 hf chainhero io tlscacerts certificate location absolute path path gopath src github com chainhero heroes service fixtures crypto config peerorganizations org1 hf chainhero io ca ca org1 hf chainhero io cert pem entitymatchers peer pattern w peer0 org1 hf chainhero io w urlsubstitutionexp localhost 7051 eventurlsubstitutionexp localhost 7053 ssltargetoverrideurlsubstitutionexp peer0 org1 hf chainhero io mappedhost peer0 org1 hf chainhero io pattern w peer1 org1 hf chainhero io w urlsubstitutionexp localhost 8051 eventurlsubstitutionexp localhost 8053 ssltargetoverrideurlsubstitutionexp peer1 org1 hf chainhero io mappedhost peer1 org1 hf chainhero io orderer pattern w orderer hf chainhero io w urlsubstitutionexp localhost 7050 ssltargetoverrideurlsubstitutionexp orderer hf chainhero io mappedhost orderer hf chainhero io certificateauthorities pattern w ca org1 hf chainhero io w urlsubstitutionexp http localhost 7054 mappedhost ca org1 hf chainhero io the configuration file is also available here config yaml config yaml b initialise we add a new folder named blockchain that will contain the whole interface that communicate with the network we will see the fabric sdk go only in this folder bash mkdir gopath src github com chainhero heroes service blockchain now we add a new go file named setup go bash vi gopath src github com chainhero heroes service blockchain setup go go package blockchain import fmt mspclient github com hyperledger fabric sdk go pkg client msp github com hyperledger fabric sdk go pkg client resmgmt github com hyperledger fabric sdk go pkg common errors retry github com hyperledger fabric sdk go pkg common providers msp github com hyperledger fabric sdk go pkg core config github com hyperledger fabric sdk go pkg fabsdk github com pkg errors fabricsetup implementation type fabricsetup struct configfile string orgid string ordererid string channelid string chaincodeid string initialized bool channelconfig string chaincodegopath string chaincodepath string orgadmin string orgname string username string admin resmgmt client sdk fabsdk fabricsdk initialize reads the configuration file and sets up the client chain and event hub func setup fabricsetup initialize error add parameters for the initialization if setup initialized return errors new sdk already initialized initialize the sdk with the configuration file sdk err fabsdk new config fromfile setup configfile if err nil return errors withmessage err failed to create sdk setup sdk sdk fmt println sdk created the resource management client is responsible for managing channels create update channel resourcemanagerclientcontext setup sdk context fabsdk withuser setup orgadmin fabsdk withorg setup orgname if err nil return errors withmessage err failed to load admin identity resmgmtclient err resmgmt new resourcemanagerclientcontext if err nil return errors withmessage err failed to create channel management client from admin identity setup admin resmgmtclient fmt println ressource management client created the msp client allow us to retrieve user information from their identity like its signing identity which we will need to save the channel mspclient err mspclient new sdk context mspclient withorg setup orgname if err nil return errors withmessage err failed to create msp client adminidentity err mspclient getsigningidentity setup orgadmin if err nil return errors withmessage err failed to get admin signing identity req resmgmt savechannelrequest channelid setup channelid channelconfigpath setup channelconfig signingidentities msp signingidentity adminidentity txid err setup admin savechannel req resmgmt withordererendpoint setup ordererid if err nil txid transactionid return errors withmessage err failed to save channel fmt println channel created make admin user join the previously created channel if err setup admin joinchannel setup channelid resmgmt withretry retry defaultresmgmtopts resmgmt withordererendpoint setup ordererid err nil return errors withmessage err failed to make admin join channel fmt println channel joined fmt println initialization successful setup initialized true return nil func setup fabricsetup closesdk setup sdk close the file is available here blockchain setup go blockchain setup go at this stage we only initialized a client that will communicate to a peer a ca and an orderer we also made a new channel and connected this peer to this channel see the comments in the code for more information c test to make sure that the client managed to initialize all his components we will make a simple test with the network launched in order to do this we need to build the go code since we we haven t any main file we have to add one bash cd gopath src github com chainhero heroes service vi main go go package main import fmt github com chainhero heroes service blockchain os func main definition of the fabric sdk properties fsetup blockchain fabricsetup network parameters ordererid orderer hf chainhero io channel parameters channelid chainhero channelconfig os getenv gopath src github com chainhero heroes service fixtures artifacts chainhero channel tx chaincode parameters chaincodeid heroes service chaincodegopath os getenv gopath chaincodepath github com chainhero heroes service chaincode orgadmin admin orgname org1 configfile config yaml user parameters username user1 initialization of the fabric sdk from the previously set properties err fsetup initialize if err nil fmt printf unable to initialize the fabric sdk v n err return close sdk defer fsetup closesdk the file is available here main go main go the last thing to do before starting the compilation is to use a vendor directory that will contain all our dependencies in our gopath we have fabric sdk go and maybe other projects when we will try to compile our app golang search dependencies in our gopath but first it checks if there is a vendor folder in the project if the dependency is satisfied then golang doesn t go looking in gopath or goroot this is very useful when using several different versions of a dependency some conflicts can happen like multiple definitions of bccsp in our case we will handle this by using a tool like dep https github com golang dep to flatten these dependencies in the vendor directory when you installed the sdk dependencies dep was automatically installed if this is not the case you can install it by reading the instructions available here dep installation https github com golang dep installation create a file called gopkg toml and copy this inside bash cd gopath src github com chainhero heroes service vi gopkg toml toml ignored github com chainhero heroes service chaincode constraint release v1 0 0 alpha4 name github com hyperledger fabric sdk go revision a906355f73d060d7bf95874a9e90dc17589edbb3 this is a constraint for dep in order to specify that in our vendor we want the sdk go to a specific version save the file and then execute this command to synchronize the vendor directory with our project s dependencies this may take a while to proceed bash cd gopath src github com chainhero heroes service dep ensure now we can compile our application bash cd gopath src github com chainhero heroes service go build after some time a new binary named main will appear at the root of the project try to start the binary like this bash cd gopath src github com chainhero heroes service heroes service screenshot app started but no network docs images start app no network png at this point it won t work because there is no network deployed that the sdk can talk with we will first start the network and then launch the app again bash cd gopath src github com chainhero heroes service fixtures docker compose up d cd heroes service screenshot app started and sdk initialised docs images start app initialized png note you need to see initialization successful if it s not the case then something went wrong alright so we just initialised the sdk with our local network in the next step we will interact with a chaincode d clean up and makefile the fabric sdk generates some files like certificates binaries and temporally files shutting down the network won t fully clean up your environment and when you will need to start it again these files will be reused to avoid building process for development you can keep them to test quickly but for a real test you need to clean up all and start from the beginning how to clean up my environment shut down your network cd gopath src github com chainhero heroes service fixtures docker compose down remove credential stores defined in the config config yaml file in client credentialstore section rm rf tmp heroes service remove some docker containers and docker images not generated by the docker compose command bash docker rm f v docker ps a no trunc grep heroes service cut d f 1 2 dev null and bash docker rmi docker images no trunc grep heroes service cut d f 1 2 dev null how to be more efficient we can automatize all these tasks in one single step also the build and start process can be automated to do so we will create a makefile first ensure that you have the tool bash make version if make is not installed do ubuntu bash sudo apt install make then create a file named makefile at the root of the project with this content bash cd gopath src github com chainhero heroes service vi makefile makefile phony all dev clean build env up env down run all clean build env up run dev build run build build echo build dep ensure go build echo build done env env up echo start environment cd fixtures docker compose up force recreate d echo environment up env down echo stop environment cd fixtures docker compose down echo environment down run run echo start app heroes service clean clean env down echo clean up rm rf tmp heroes service heroes service docker rm f v docker ps a no trunc grep heroes service cut d f 1 2 dev null true docker rmi docker images no trunc grep heroes service cut d f 1 2 dev null true echo clean up done the file is available here makefile makefile now with the task all 1 the whole environment will be cleaned up 2 then our go program will be compiled 3 after which the network will be deployed and 4 finally the app will be up and running to use it go in the root of the project and use the make command task all make or make all task clean clean up everything and put down the network make clean task build just build the application make build task env up just make the network up make env up e install instantiate the chaincode we are almost there to use the blockchain system but for now we haven t set up any chaincode smart contract yet that will handle queries from our application first let s create a new directory named chaincode and add a new file named main go this is the main entry point of our smart contract bash cd gopath src github com chainhero heroes service mkdir chaincode vi chaincode main go go package main import fmt github com hyperledger fabric core chaincode shim pb github com hyperledger fabric protos peer heroesservicechaincode implementation of chaincode type heroesservicechaincode struct init of the chaincode this function is called only one when the chaincode is instantiated so the goal is to prepare the ledger to handle future requests func t heroesservicechaincode init stub shim chaincodestubinterface pb response fmt println heroesservicechaincode init get the function and arguments from the request function stub getfunctionandparameters check if the request is the init function if function init return shim error unknown function call put in the ledger the key value hello world err stub putstate hello byte world if err nil return shim error err error return a successful message return shim success nil invoke all future requests named invoke will arrive here func t heroesservicechaincode invoke stub shim chaincodestubinterface pb response fmt println heroesservicechaincode invoke get the function and arguments from the request function args stub getfunctionandparameters check whether it is an invoke request if function invoke return shim error unknown function call check whether the number of arguments is sufficient if len args 1 return shim error the number of arguments is insufficient in order to manage multiple type of request we will check the first argument here we have one possible argument query every query request will read in the ledger without modification if args 0 query return t query stub args return shim error unknown action check the first argument query every readonly functions in the ledger will be here func t heroesservicechaincode query stub shim chaincodestubinterface args string pb response fmt println heroesservicechaincode query check whether the number of arguments is sufficient if len args 2 return shim error the number of arguments is insufficient like the invoke function we manage multiple type of query requests with the second argument we also have only one possible argument hello if args 1 hello get the state of the value matching the key hello in the ledger state err stub getstate hello if err nil return shim error failed to get state of hello return this value in response return shim success state if the arguments given don t match any function we return an error return shim error unknown query action check the second argument func main start the chaincode and make it ready for futures requests err shim start new heroesservicechaincode if err nil fmt printf error starting heroes service chaincode s err the file is available here chaincode main go chaincode main go note the chaincode isn t really related to the application we can have one repository for the app and another for the chaincode for your information today the chaincode can also be written in other languages like java for now the chaincode does nothing extraordinary just put the key value hello world in the ledger at initialization in addition there is one function that we can call by an invoke query hello this function gets the state of the ledger i e hello and give it in response we will test this in the next step after successfully install and instantiate the chaincode in order to install and instantiate the chaincode we need to add some code in the application edit the blockchain setup go blockchain setup go with those following lines go package blockchain import fmt github com hyperledger fabric sdk go pkg client channel github com hyperledger fabric sdk go pkg client event mspclient github com hyperledger fabric sdk go pkg client msp github com hyperledger fabric sdk go pkg client resmgmt github com hyperledger fabric sdk go pkg common errors retry github com hyperledger fabric sdk go pkg common providers msp github com hyperledger fabric sdk go pkg core config packager github com hyperledger fabric sdk go pkg fab ccpackager gopackager github com hyperledger fabric sdk go pkg fabsdk github com hyperledger fabric sdk go third party github com hyperledger fabric common cauthdsl github com pkg errors fabricsetup implementation type fabricsetup struct configfile string orgid string ordererid string channelid string chaincodeid string initialized bool channelconfig string chaincodegopath string chaincodepath string orgadmin string orgname string username string client channel client admin resmgmt client sdk fabsdk fabricsdk event event client func setup fabricsetup installandinstantiatecc error create the chaincode package that will be sent to the peers ccpkg err packager newccpackage setup chaincodepath setup chaincodegopath if err nil return errors withmessage err failed to create chaincode package fmt println ccpkg created install example cc to org peers installccreq resmgmt installccrequest name setup chaincodeid path setup chaincodepath version 0 package ccpkg err setup admin installcc installccreq resmgmt withretry retry defaultresmgmtopts if err nil return errors withmessage err failed to install chaincode fmt println chaincode installed set up chaincode policy ccpolicy cauthdsl signedbyanymember string org1 hf chainhero io resp err setup admin instantiatecc setup channelid resmgmt instantiateccrequest name setup chaincodeid path setup chaincodegopath version 0 args byte byte init policy ccpolicy if err nil resp transactionid return errors withmessage err failed to instantiate the chaincode fmt println chaincode instantiated channel client is used to query and execute transactions clientcontext setup sdk channelcontext setup channelid fabsdk withuser setup username setup client err channel new clientcontext if err nil return errors withmessage err failed to create new channel client fmt println channel client created creation of the client which will enables access to our channel events setup event err event new clientcontext if err nil return errors withmessage err failed to create new event client fmt println event client created fmt println chaincode installation instantiation successful return nil the file is available here blockchain setup go blockchain setup go tips take care of the chaincode version if you want to update your chaincode increment the version number set at the line 105 of this setup go blockchain setup go file otherwise the network will keep the same chaincode we need now to modify our main go file in order to call our new function bash cd gopath src github com chainhero heroes service vi main go go package main import fmt github com chainhero heroes service blockchain os func main definition of the fabric sdk properties fsetup blockchain fabricsetup network parameters ordererid orderer hf chainhero io channel parameters channelid chainhero channelconfig os getenv gopath src github com chainhero heroes service fixtures artifacts chainhero channel tx chaincode parameters chaincodeid heroes service chaincodegopath os getenv gopath chaincodepath github com chainhero heroes service chaincode orgadmin setup orgadmin orgname org1 configfile config yaml user parameters username user1 install and instantiate the chaincode err fsetup installandinstantiatecc if err nil fmt printf unable to install and instantiate the chaincode v n err return the file is available here main go main go we can test this just with the make command setup in the previous step bash cd gopath src github com chainhero heroes service make screenshot chaincode installed and instantiated docs images install and instantiate cc png tips the installation and the instantiation don t need to be run at every start of the application only when we update the chaincode and the chaincode version a solution is to provide an argument when we run the application to tell to do this additional procedure before move on since in this tutorial we will clean up the environment every time we don t really care about that f query the chaincode like a database the chaincode is plugged and ready to answer let s try the hello query we will put all query functions in a new file named query go in the blockchain folder bash cd gopath src github com chainhero heroes service vi blockchain query go go package blockchain import fmt github com hyperledger fabric sdk go pkg client channel queryhello query the chaincode to get the state of hello func setup fabricsetup queryhello string error prepare arguments var args string args append args invoke args append args query args append args hello response err setup client query channel request chaincodeid setup chaincodeid fcn args 0 args byte byte args 1 byte args 2 if err nil return fmt errorf failed to query v err return string response payload nil the file is available here blockchain query go blockchain query go you can add the call to this new function in the main go main go bash cd gopath src github com chainhero heroes service vi main go go func main query the chaincode response err fsetup queryhello if err nil fmt printf unable to query hello on the chaincode v n err else fmt printf response from the query hello s n response let s try bash cd gopath src github com chainhero heroes service make screenshot query hello docs images query hello png g change the ledger state the next thing to do in order to make a basic tour of the fabric sdk go is to make a request to the chaincode in order to change the ledger state first we will add this ability in the chaincode edit the chaincode main go chaincode main go file bash cd gopath src github com chainhero heroes service vi chaincode main go go invoke all future requests named invoke will arrive here func t heroesservicechaincode invoke stub shim chaincodestubinterface pb response fmt println heroesservicechaincode invoke get the function and arguments from the request function args stub getfunctionandparameters check whether it is an invoke request if function invoke return shim error unknown function call check whether the number of arguments is sufficient if len args 1 return shim error the number of arguments is insufficient in order to manage multiple type of request we will check the first argument here we have one possible argument query every query request will read in the ledger without modification if args 0 query return t query stub args the update argument will manage all update in the ledger if args 0 invoke return t invoke stub args if the arguments given don t match any function we return an error return shim error unknown action check the first argument invoke every functions that read and write in the ledger will be here func t heroesservicechaincode invoke stub shim chaincodestubinterface args string pb response fmt println heroesservicechaincode invoke if len args 2 return shim error the number of arguments is insufficient check if the ledger key is hello and process if it is the case otherwise it returns an error if args 1 hello len args 3 write the new value in the ledger err stub putstate hello byte args 2 if err nil return shim error failed to update state of hello notify listeners that an event eventinvoke have been executed check line 19 in the file invoke go err stub setevent eventinvoke byte if err nil return shim error err error return this value in response return shim success nil if the arguments given don t match any function we return an error return shim error unknown invoke action check the second argument the file is available here chaincode main go chaincode main go from the application side we add a new function to make the invocation of the chaincode add a file named invoke go in the blockchain folder bash cd gopath src github com chainhero heroes service vi blockchain invoke go go package blockchain import fmt github com hyperledger fabric sdk go pkg client channel time invokehello func setup fabricsetup invokehello value string string error prepare arguments var args string args append args invoke args append args invoke args append args hello args append args value eventid eventinvoke add data that will be visible in the proposal like a description of the invoke request transientdatamap make map string byte transientdatamap result byte transient data in hello invoke reg notifier err setup event registerchaincodeevent setup chaincodeid eventid if err nil return err defer setup event unregister reg create a request proposal and send it response err setup client execute channel request chaincodeid setup chaincodeid fcn args 0 args byte byte args 1 byte args 2 byte args 3 transientmap transientdatamap if err nil return fmt errorf failed to move funds v err wait for the result of the submission select case ccevent notifier fmt printf received cc event s n ccevent case time after time second 20 return fmt errorf did not receive cc event for eventid s eventid return string response transactionid nil the file is available here blockchain invoke go blockchain invoke go you can then add the call to this function in the main go main go bash cd gopath src github com chainhero heroes service vi main go go func main query the chaincode response err fsetup queryhello if err nil fmt printf unable to query hello on the chaincode v n err else fmt printf response from the query hello s n response invoke the chaincode txid err fsetup invokehello chainhero if err nil fmt printf unable to invoke hello on the chaincode v n err else fmt printf successfully invoke hello transaction id s n txid query again the chaincode response err fsetup queryhello if err nil fmt printf unable to query hello on the chaincode v n err else fmt printf response from the query hello s n response let s try bash cd gopath src github com chainhero heroes service make screenshot invoke hello docs images invoke hello png note this error message may appear endorsement validation failed endorser client status code 3 endorsement mismatch description proposalresponsepayloads do not match since we have two peers the sdk sends its request to the two directly and if it receives a different answer which is quite possible because it s an asynchronous system then it returns this error the best thing to do in these cases is to start over again 6 make this in a web application we also can make this usable for any user the best choice is a web application and we are lucky because the go language natively provides a web server handling http requests and also templating for html for now we have only two different actions the query and the invocation of the hello value let s make two html pages for each action we add a web web directory with three other directories web templates web templates contains all html pages templates web assets web assets contains all css javascript fonts images web controllers web controllers contains all functions that will render templates we use the mvc model view controller to make it more readable the model will be the blockchain part the view are templates and controller are provided by functions in the controllers web controllers directory populate each with the appropriate code we also added bootstrap to make the result a little prettier web templates layout html web templates layout html web templates home html web templates home html web templates request html web templates request html web controllers controller go web controllers controller go web controllers home go web controllers home go web controllers request go web controllers request go web app go web app go web assets web assets and finally we change the main go main go in order to use the web interface instead of directly query the blockchain main go main go run the app and go to localhost 3000 home html http localhost 3000 home html bash cd gopath src github com chainhero heroes service make the home page make a query in the blockchain to get the value of the hello key and display it screenshot web home hello world docs images web home hello world png the request page has a form to change the hello value screenshot web request write docs images web request write png after a successful submission the transaction id is given screenshot web request success docs images web request success png we can see the change by going back to the home page screenshot web home hello superman docs images web home hello superman png it s the end for the first part a more complex application is coming 7 references hyperledger website https www hyperledger org hyperledger fabric online documentation http hyperledger fabric readthedocs io en latest hyperledger fabric on github https github com hyperledger fabric hyperledger fabric certificate authority on github https github com hyperledger fabric ca hyperledger fabric sdk go on github https github com hyperledger fabric sdk go fabric sdk go tests https github com hyperledger fabric sdk go blob master test integration end to end test go cli https github com securekey fabric examples tree master fabric cli an example cli for fabric built with the go sdk | blockchain golang hyperledger-fabric hyperledger tutorial | blockchain |
ai-tutor | ai tutor a prompt for configuring your personal teacher this repository provides you with a prompt designed to help you learn and practice new skills using chatgpt the prompt has been primarily tested with gpt 3 5 and it has been created to offer clear and specific information about a topic ask questions to understand your learning needs create long course plans to help you hit large goals create smaller learning modules teach individual steps with a mix of theory and practice test your knowledge through a series of questions walk you through practical exercises how to use the prompt 1 to get started copy the code from the init md markdown file https raw githubusercontent com codewithjv ai tutor main init md file within this repository if you want to modify the prompt do so all in one place as i will be releasing updated versions as i get more feedback and it will be easier for you to take advantage of them if your edits are in one spot 2 open up a new chatgpt session and paste the prompt in 3 you can now interact with the ai tutor and ask it to do things like i would like to build project description i already know previous experience what should i learn please create a course for topic please create a stand alone module for topic please create my study plan in a code block this makes it easy to copy and paste into your editor please teach me step number please provide some practice exercises for step number please test my knowledge of step i would like step xyx to have more less detail 4 you can also prefix an instruction before pasting the prompt such as teach me the fundamentals of coding using python using the following guidelines which will give you better chat titles if you alway slead with the guidelines you will have lots of chats called ai tutor guidelines you can also paste in your study plan or subset and then the guidelines 5 chatgpt can get forgetful over long chat sessions so you can try things like please print out a summary of our lesson plan what is your understanding of the guidelines around creating lesson plans or teaching or testing etc please remember your guidelines are and paste in the entire prompt or a subsection chatgpt versions and other llms the prompt tends to work better with gpt 4 but is often used with gpt 3 5 as it is good enough and doesn t use your quota switch to gpt 4 if gpt 3 5 is being uncooperative i haven t taken the prompt for a spin on other llms and would love to hear how it performs if you do so a note on markdown markdown is a lightweight text formatting syntax designed to be both human readable and easily convertible to html it allows you to write and structure content using simple punctuation and characters you can edit markdown files using any text editor such as visual studio code notepad or sublime text to learn more about markdown and its syntax you can visit the official markdown guide https www markdownguide org or browse other resources and tutorials available online i created this repo as part of the codewithjv com http codewithjv com project where i am exploring the impacts of ai tools on education and teaching people how to code sign up if you want a weekly email http codewithjv com newsletter on learning to code using ai assistants or subscribe on youtube https www youtube com learncodewithjv for more good stuff | ai |
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Udacity-Data-Engineering-Projects | data engineering projects https github com san089 udacity data engineering projects blob master image jpeg project 1 data modeling with postgres in this project we apply data modeling with postgres and build an etl pipeline using python a startup wants to analyze the data they ve been collecting on songs and user activity on their new music streaming app currently they are collecting data in json format and the analytics team is particularly interested in understanding what songs users are listening to link data modeling with postgres https github com san089 udacity data engineering projects tree master data modeling with postgres project 2 data modeling with cassandra in this project we apply data modeling with cassandra and build an etl pipeline using python we will build a data model around our queries that we want to get answers for for our use case we want below answers get details of a song that was herad on the music app history during a particular session get songs played by a user during particular session on music app get all users from the music app history who listened to a particular song link data modeling with apache cassandra https github com san089 udacity data engineering projects tree master data modeling with apache cassandra project 3 data warehouse in this project we apply the data warehouse architectures we learnt and build a data warehouse on aws cloud we build an etl pipeline to extract and transform data stored in json format in s3 buckets and move the data to warehouse hosted on amazon redshift use redshift iac script redshift iac readme https github com san089 udacity data engineering projects blob master redshift iac readme md link data warehouse https github com san089 udacity data engineering projects tree master data warehouse project 4 data lake in this project we will build a data lake on aws cloud using spark and aws emr cluster the data lake will serve as a single source of truth for the analytics platform we will write spark jobs to perform elt operations that picks data from landing zone on s3 and transform and stores data on the s3 processed zone link data lake https github com san089 udacity data engineering projects tree master data lake project 5 data pipelines with airflow in this project we will orchestrate our data pipeline workflow using an open source apache project called apache airflow we will schedule our etl jobs in airflow create project related custom plugins and operators and automate the pipeline execution link airflow data pipelines https github com san089 udacity data engineering projects tree master airflow data pipelines capstone project udacity provides their own crafted capstone project with dataset that include data on immigration to the united states and supplementary datasets that include data on airport codes u s city demographics and temperature data i worked on my own open ended project here is the link goodreads etl pipeline https github com san089 goodreads etl pipeline | cloud |
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countcolor 23697689 style flat don t forget to hit the star if you like this repo software engineering wbl course synopsis this course is designed to give students an introduction to an engineering approach in the development of high quality software systems it will discuss the important software engineering concepts in the various types of the common software process models the students will also learn the concepts and techniques used in each software development phase including requirements engineering software design and software testing this course will also expose the students to utilizing object oriented method e g uml and tools in analyzing and designing the software at the end of this course students are expected to be able to appreciate most of the common software engineering concepts and techniques as well as producing various software artifacts and deliverables course learning outcomes 1 apply the fundamental of software engineering software process requirements engineering to solve software engineering case studies 2 build suitable software architecture object oriented design model and develop test cases in software engineering case studies 3 construct software requirements model software architecture object oriented design model and test cases with state of the art methods and tools for a real world software engineering problem 4 ability to establish good rapport interact with others work effectively in a team and comprehend the interchangeable role of leaders and followers with team members important things 1 lecture notes https drive google com drive folders 1ffqvsa7wdq2uet0udvbqcaluvcqme6gw usp sharing 2 student information section 01 materials student 01 md section 02 materials student 02 md 3 task 1 additional notes materials task1 md 4 lab exercise lab 5 project 1 student portfolio showcase html project 1 portfolio readme md 6 project 2 student portfolio showcase bootstrap project 2 bootstrap readme md 7 student portfolio showcase https drshahizan github io 8 book materials book md 9 software requirements document srd academic course registration system materials srd sample md 10 class exercise module 4 materials module4 11 carry marks images se cm pdf weekly schedule no module description 01 02 1 1 introduction to software engineering materials module1 readme md software definition software engineering as a layered technology types of software inherent difficulties in software engineering se quality focus documentation standard a href https github com drshahizan software engineering blob main materials sec01 mod1 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod1 md img src images markdownh png width 24px height 24px a 2 proposal proposal a proposal in software engineering is a document that outlines a proposed solution to a problem or need related to software development it typically includes an overview of the problem the proposed solution the benefits and risks of the proposed solution the resources required to implement the solution and a plan for implementing and testing the solution 2 2 software process model plan driven or agile process model general software process model waterfall incremental reuse oriented software process model which cope with change spiral model and rational unified process a href https github com drshahizan software engineering blob main materials sec01 mod2 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod2 md img src images markdownh png width 24px height 24px a 3 3 agile software development agile methods agile development techniques agile project management scaling agile methods a href https github com drshahizan software engineering blob main materials sec01 mod3 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod3 md img src images markdownh png width 24px height 24px a 4 4 requirements engineering materials module4 readme md types of requirements functional and non functional requirements requirements specification requirements engineering processes a href https github com drshahizan software engineering blob main materials sec01 mod4 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod4 md img src images markdownh png width 24px height 24px a 5 uml and tools materials uml a uml unified modeling language diagram is a visual representation of a system or process that uses standardized symbols and notations to depict the structure behavior and relationships between different components of the system it provides a clear and concise way to communicate and document the design and architecture of a software system enabling stakeholders to understand and analyze the system s various aspects 6 5 requirements analysis and modelling me materials module5 use case modelling and specification domain modelling activity diagram sequence diagram state chart state machine diagram a href https github com drshahizan software engineering blob main materials sec01 mod5 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod5 md img src images markdownh png width 24px height 24px a 7 figma materials figma md learn figma 8 krisa materials krisa md metodologi krisa meliputi 6 fasa utama yang perlu difahami diguna pakai dan dilaksanakan oleh agensi setiap fasa menerangkan aktiviti teknik dan pendekatan serta dokumentasi serahan 9 6 architectural design materials module6 architectural design and detailed design architectural design decision architectural view component diagram in relation to architectural view architectural pattern model view controller mvc layered repository client server and pipe filter a href https github com drshahizan software engineering blob main materials sec01 mod6 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod6 md img src images markdownh png width 24px height 24px a 10 7 object oriented detailed design relationships among analysis design and implementation object oriented design using uml object oriented design principles elaborating uml diagrams from analysis a href https github com drshahizan software engineering blob main materials sec01 mod7 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod7 md img src images markdownh png width 24px height 24px a 11 uml exercise the uml exercise is designed to familiarize students with unified modeling language uml a standardized visual modeling language used in software engineering through this exercise students gain hands on experience in creating uml diagrams such as class diagrams sequence diagrams and use case diagrams by applying uml students can effectively analyze design and communicate software system structures and behaviors 12 8 software verification validation and testing introduction to verification and validation verification and validation planning software inspections system testing component testing test case design test case design using black box and white box a href https github com drshahizan software engineering blob main materials sec01 mod8 md img src images markdownp png width 24px height 24px a a href https github com drshahizan software engineering blob main materials sec02 mod8 md img src images markdownh png width 24px height 24px a 13 software engineering project test case design produce testing document that includes all the required test cases 14 project demonstration and hand over project no module description file 1 project 1 student portfolio showcase html a href project 1 portfolio img src images html1 png width 24px height 24px a 2 project 2 student portfolio showcase bootstrap a href project 2 bootstrap img src images php64 png width 24px height 24px a 3 proposal guideline for software engineering proposal a href proposal materials guideline md img src images rfp png width 24px height 24px a 4 system documentation system requirements specification srs system design document sdd and system testing document std a href project documentation img src images rfp png width 24px height 24px a submission no topic file 1 proposal a href proposal materials groups md img src images rfp png width 24px height 24px a 2 system requirements specification srs a href project documentation srs srs group md img src images document1 png width 24px height 24px a 3 system design descriptions sdd a href project documentation sdd sdd group md img src images document1 png width 24px height 24px a 4 system test descriptions std a href project documentation std std group md img src images document1 png width 24px height 24px a 5 project a href project project img src images document png width 24px height 24px a tools diagrams are visual representations of information or data that help convey complex concepts processes or systems in a clear and concise manner flowcharts are diagrams that use shapes and arrows to illustrate the steps in a process or algorithm more info materials tools md no tools file 1 figma a href materials figma md img src images figma svg width 24px height 24px a 2 draw io a href materials uml drawio 1 draw io md img src images drawio svg width 24px height 24px a 3 github pages a href https github com drshahizan learn github blob main materials pages md img src images github svg width 24px height 24px a 4 behance a href materials behance md img src images behance svg width 24px height 24px a 5 visual studio code a href https code visualstudio com img src images vsc svg width 24px height 24px a 6 bootstrap studio a href https bootstrapstudio io img src images bootstrap studio png width 24px height 24px a 7 carbon a href https carbon now sh img src images carbon svg width 24px height 24px a contribution please create an issue https github com drshahizan software engineering issues for any improvements suggestions or errors in the content you can also contact me using linkedin https www linkedin com in drshahizan for any other queries or feedback visitors https api visitorbadge io api visitors path https 3a 2f 2fgithub com 2fdrshahizan labelcolor 23697689 countcolor 23555555 style plastic https visitorbadge io status path https 3a 2f 2fgithub com 2fdrshahizan https hit yhype me github profile user id 81284918 | software-engineering uml | os |
web-development-with-django-cookbook | web development with django cookbook https www packtpub com sites default files 6898os web 20development 20with 20django 20cookbook cover 0 jpg review it s a great book to start learning django i like the fact that it s simple i don t need too much of a machine environnment some book uses redis docker rabitmq and pretend they are for beginners i didn t need all thoses software for this book with the exception of one of the chapter that mention memcached this book is more like an intro that s what i like about this book for some scripts in this book it is better to use a django package like django rosetta model translation or django extensions create modified date but this book is dead simple it gives you a way to do it without mentionning those django packages i didn t like the fact that sometimes i wanted to do a copy paste but the copy paste from the pdf to my code editor doesn t indent properly the code this book should be the first book or one of the first book to read after doing the basic django tutorial available on the django site the price is 29 which is afortable what i don t like is that i had to use python 2 they wrote that i could use python 3 but from chapter 1 to 4 i got a lots of bug the chapter 1 and 2 i didn t have a lot but on chapter 3 and 4 i had a lots that why i didn t do the rest of the chapters 5 11 in python 3 so i prefer to do everything on python by installing continuum anaconda i give this book a 4 on 5 i didn t quite like the fact that this book change so often the django version 1 8 1 1 8 4 they didn t tell what was the difference between those version for the requirements they were exact at 95 i had to change the django mptt version open notes todo | front_end |
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proj-hypervisor-in-zephyr | proj hypervisor in zephyr zephyr rtos hypervisor linux zephyr zephyr zephyr based virtual machine zvm kernel based virtual machine kvm zephyr c c zephyr rtos zephyr rtos apache license 2 0 zephyr zephyr rtos zephyr linux kvm zephyr rtos kvm 1 https docs zephyrproject org latest 2 kvm in linux 3 https gitee com openeuler zvm 2023 os 2023 2023 xgqman hnu edu cn 1 c c 2 x86 arm riscv 3 qemu 4 zephyr license apache license 2 0 license 1 qemu linux zephyr 2 3 4 5 virtio | os |
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ksql-udf-deep-learning-mqtt-iot | deep learning udf for ksql ksqldb for streaming anomaly detection of mqtt iot sensor data i built a ksql udf for sensor analytics it leverages the new api features of ksql to build udf udaf functions easily with java https docs confluent io current ksql docs udf html to do continuous stream processing on incoming events if you want to build your own udf please check out this blog post for a detailed how to and potential issues during development and testing how to build a udf and or udaf in ksql 5 0 https www confluent io blog build udf udaf ksql 5 0 use case connected cars real time streaming analytics using deep learning continuously process millions of events from connected devices sensors of cars in this example pictures connected cars iot deep learning png architecture sensor data via confluent mqtt proxy to kafka cluster for ksql processing and real time analytics this project focuses on the ingestion of data into kafka via mqtt and processing of data via ksql pictures mqtt proxy confluent cloud png live demo video mqtt with kafka connect and mqtt proxy if you want to see apache kafka mqtt integration in a video please check out the following 15min recording showing a demo my two github examples apache kafka mqtt integration pictures mqtt apache kafka integration confluent proxy connect png https www youtube com watch v l38 6ilgeke source code here is the full source code for the anomaly detection ksql udf https github com kaiwaehner ksql udf deep learning mqtt iot blob master src main java com github megachucky kafka streams machinelearning anomaly java it is pretty easy to develop udfs just implement the function in one java method within a udf class udf description apply analytic model to sensor input public string anomaly udfparameter sensorinput string sensorinput your business logic how to run it requirements the code is developed and tested on mac and linux operating systems as kafka does not support and work well on windows this is not tested at all java 8 confluent platform 5 4 https www confluent io download confluent enterprise if you want to use the confluent mqtt proxy confluent open source if you just want to run the ksql udf and send data via kafkacat instead of mqtt mqtt client i use mosquitto https mosquitto org download in the demo as mqtt client to publish mqtt messages i don t even start the mqtt server thus you can also use any other mqtt client instead kafkacat https github com edenhill kafkacat optional if you do not want to use mqtt producers and of course you can also use kafka console producer instead but kafkacat is much more comfortable step by step demo install confluent platform https www confluent io download and mosquitto https mosquitto org download or any other mqtt client then follow these steps to deploy the udf create mqtt events and process them via ksql leveraging the analytic model https github com kaiwaehner ksql udf deep learning mqtt iot blob master live demo ksql udf deep learning mqtt iot adoc other information and projects related to kafka and mqtt if you want to find more details about kafka mqtt integration take a look at my slides from kafka summit 2018 in san francisco iot integration with mqtt and apache kafka https www slideshare net kaiwaehner iot integration with mqtt and apache kafka the video recording is available on the website of kafka summit for free kafka mqtt integration video recording https www confluent io kafka summit sf18 processing iot data from end to end to see the other part integration with sink applications like elasticsearch grafana please take a look at the project ksql for streaming iot data https github com kaiwaehner ksql fork with deep learning function which shows how to realize the integration with elasticsearch via kafka connect the github project apache kafka kafka connect mqtt connector sensor data https github com kaiwaehner kafka connect iot mqtt connector example also integrates with mqtt devices though this project uses confluent s mqtt connector for kafka connect i e a different approach where you use a mqtt broker in between the devices and kafka if you want to see a more powerful and complete demo check out streaming machine learning at scale from 100000 iot devices with hivemq apache kafka and tensorflow https github com kaiwaehner hivemq mqtt tensorflow kafka realtime iot machine learning training inference | kafka kafka-connect kafka-client confluent confluent-platform open-source deep-learning machine-learning tensorflow h2oai java ksql mqtt | server |
Udagram | local url http localhost 8082 filteredimage image url https picsum photos 200 300 jpg web url http udagram jan dev dev us east 1 elasticbeanstalk com filteredimage image url https picsum photos 200 300 jpg udagram image filtering microservice udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into three parts 1 the simple frontend https github com udacity cloud developer tree master course 02 exercises udacity c2 frontend a basic ionic client web application which consumes the restapi backend covered in the course 2 the restapi backend https github com udacity cloud developer tree master course 02 exercises udacity c2 restapi a node express server which can be deployed to a cloud service covered in the course 3 the image filtering microservice https github com udacity cloud developer tree master course 02 project image filter starter code the final project for the course it is a node express application which runs a simple script to process images my assignment tasks setup node environment you ll need to create a new node server open a new terminal within the project directory and run 1 initialize a new project npm i 2 run the development server with npm run dev create a new endpoint in the server ts file the starter code has a task for you to complete an endpoint in src server ts which uses query parameter to download an image from a public url filter the image and return the result we ve included a few helper functions to handle some of these concepts and we re importing it for you at the top of the src server ts file typescript import filterimagefromurl deletelocalfiles from util util deploying your system follow the process described in the course to eb init a new application and eb create a new environment to deploy your image filter service don t forget you can use eb deploy to push changes stand out optional refactor the course restapi if you re feeling up to it refactor the course restapi to make a request to your newly provisioned image server authentication prevent requests without valid authentication headers note if you choose to submit this make sure to add the token to the postman collection and export the postman collection file to your submission so we can review custom domain name add your own domain name and have it point to the running services try adding a subdomain name to point to the processing server note domain names are not included in aws free tier and will incur a cost | cloud |
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e7020e-labs | e7020e labs | os |
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GreynirServer | license gpl v3 https img shields io badge license gplv3 blue svg https www gnu org licenses gpl 3 0 python 3 9 https img shields io badge python 3 9 blue svg https www python org downloads release python 390 build https github com mideind greynir actions workflows python package yml badge svg img src static img greynir logo large png alt greynir width 200 height 200 align right style margin left 20px margin bottom 20px greynirserver natural language processing for icelandic greynirserver is the web api and frontend for greynirengine https github com mideind greynirengine a natural language processing engine that extracts processable information from icelandic text allows natural language querying of that information and facilitates natural language understanding greynirserver is also the backend of embla https embla is an icelandic voice assistant app for smartphones tablets and other devices try greynir in icelandic at https greynir is https greynir is greynir periodically scrapes chunks of text from icelandic news sites on the web it employs the tokenizer https github com mideind tokenizer and greynirengine https github com mideind greynirengine modules by the same authors to tokenize the text and parse the token streams according to a hand written context free grammar for the icelandic language the resulting parse forests are disambiguated using scoring heuristics to find the best parse trees the trees are then stored in a database and processed by grammatical pattern matching modules to obtain statements of fact and relations between stated facts an overview of the technology behind greynir can be found in the paper a wide coverage context free grammar for icelandic and an accompanying parsing system https acl bg org proceedings 2019 ranlp 202019 pdf ranlp160 pdf by vilhj lmur orsteinsson hulda lad ttir and hrafn loftsson proceedings of recent advances in natural language processing pages 1397 1404 varna bulgaria sep 2 4 2019 a href https raw githubusercontent com mideind greynir master static img tree example png title greynir parse tree img src static img tree example small png width 400 height 450 alt greynir parse tree a a parse tree as displayed by greynir nouns and noun phrases are blue verbs and verb phrases are red adjectives are green prepositional and adverbial phrases are grey etc greynir is most effective for text that is objective and factual i e has a relatively high ratio of concrete concepts such as numbers amounts dates person and entity names etc greynir is innovative in its ability to parse and disambiguate text written in a morphologically rich language i e icelandic which does not lend itself easily to statistical parsing methods greynir uses grammatical feature agreement cases genders persons number singular plural verb tenses modes etc to guide and disambiguate parses its highly optimized earley based parser implemented in c is fast and compact enough to make real time while you wait analysis of web pages as well as bulk processing feasible greynir s goal is to understand text to a usable extent by parsing it into structured recursive trees that directly correspond to the original grammar these trees can then be further processed and acted upon by sets of python functions that are linked to grammar nonterminals greynir is currently able to parse about 90 of sentences in a typical news article from the web and many well written articles can be parsed completely it presently has over a million parsed articles in its database containing more than 14 million parsed sentences a relatively recent 2021 version of this database is available via the greynircorpus https github com mideind greynircorpus project greynir supports natural language querying of its databases users can ask about person names titles and entity definitions and get appropriate replies the html5 web speech api is supported to allow queries to be recognized from speech in enabled browsers such as recent versions of chrome similarity queries are also supported yielding articles that are similar in content to a given search phrase or sentence greynir may in due course be expanded for instance to make logical inferences from statements in its database to find statements supporting or refuting a thesis and or to discover contradictions between statements implementation greynir is written in python 3 https www python org except for its core earley based parser module which is written in c and called via cffi https cffi readthedocs org en latest index html greynir requires python 3 9 or later and runs on cpython and pypy http pypy org with the latter being recommended for performance reasons greynir works in stages roughly as follows 1 web scraper built on beautifulsoup http www crummy com software beautifulsoup and sqlalchemy http www sqlalchemy org storing data in postgresql http www postgresql org 2 tokenizer this one https github com mideind tokenizer extended to use the b n http bin arnastofnun is dmii database of icelandic word forms for lemmatization and initial part of speech tagging 3 parser from this module https github com mideind greynirengine using an improved version of the earley algorithm http en wikipedia org wiki earley parser to parse text according to an unconstrained hand written context free grammar for icelandic that may yield multiple parse trees a parse forest in case of ambiguity 4 parse forest reducer with heuristics to find the best parse tree 5 information extractor that maps a parse tree via its grammar constituents to plug in python functions 6 article indexer that transforms articles from bags of words to fixed dimensional topic vectors using tf idf http www tfidf com and latent semantic analysis https en wikipedia org wiki latent semantic analysis 7 query processor that supports a range of natural language queries including queries about entities in greynir s database greynir has an embedded web server that displays news articles recently scraped into its database as well as names of people extracted from those articles along with their titles the web interface enables the user to type in any url and have greynir scrape it tokenize it and display the result as a web page queries can also be entered via the keyboard or using voice input the server runs on the flask http flask pocoo org framework implements wsgi and can for instance be plugged into gunicorn http gunicorn org and nginx https www nginx com the tokenizer https github com mideind tokenizer divides text chunks into sentences and recognizes entities such as dates numbers amounts and person names as well as common abbreviations and punctuation grammar rules are laid out in a separate text file greynir grammar https github com mideind greynirengine blob master src reynir greynir grammar which is a part of greynirengine https github com mideind greynirengine the standard backus naur form http en wikipedia org wiki backus e2 80 93naur form has been augmented with repeat specifiers for right hand side tokens for 0 n instances for 1 n instances or for 0 1 instances also the grammar allows for compact specification of rules with variants for instance due to cases numbers and genders thus a single rule e g nounphrase case gender adjective case noun case gender is automatically expanded into multiple rules 12 in this case 4 cases x 3 genders with appropriate substitutions for right hand side tokens depending on their local variants the parser is an optimized c implementation of an earley parser as enhanced by scott and johnstone http www sciencedirect com science article pii s0167642309000951 referencing tomita it parses ambiguous grammars without restriction and returns a compact shared packed parse forest sppf of parse trees if a parse fails it identifies the token at which no parse was available the greynir scraper is typically run in a cron job every 30 minutes to extract articles automatically from the web parse them and store the resulting trees in a postgresql database for further processing scraper modules for new websites are plugged in by adding python code to the scrapers scrapers directory currently the scrapers default py scrapers default py module supports a wide range of popular icelandic news sites processor modules can be plugged into greynir by adding python code to the processors processors directory the module processors persons py processors persons py for example extracts person names and titles from parse trees for storage in a database table query question answering modules can be plugged into greynir by adding python code to the queries queries directory reference implementations for several query types can be found in that directory for instance queries builtin py queries builtin py which supports questions about persons and titles query module examples can be viewed in queries examples queries examples file details article py article py representation of an article through its life cycle config greynir conf config greynir conf editable configuration file db py db database models queries and functions via sqlalchemy fetcher py fetcher py utility classes for fetching articles given their urls geo py geo py geography and location related utility functions main py main py wsgi web server application and main module for command line invocation nertokenizer py nertokenizer py a layer on top of the tokenizer for named entity recognition postagger py postagger py part of speech tagging processor py processor py information extraction from parse trees and token streams queries py queries natural language query processor and query answering modules routes py routes routes for the web application scraper py scraper py web scraper collecting articles from a set of pre selected websites scrapers py scrapers scraper code for various websites settings py settings py management of global settings and configuration data speak py speak py command line interface for speech synthesis speech py speech speech synthesizer modules tnttagger py tnttagger py statistical part of speech tagging tools py tools various command line utility tools tree py tree representation of parse trees for processing and related utility functions utility py utility py assorted utility functions used throughout the codebase vectors builder py vectors builder py article indexer and lsa topic vector builder installation and setup instructions for ubuntu debian gnu linux docs setup linux md instructions for macos docs setup macos md docker container https github com vthorsteinsson greynir docker running greynir once you have followed the installation and setup instructions above change to the greynir repository and activate the virtual environment bash cd greynir source venv bin activate you should now be able to run greynir web application bash python main py defaults to running on localhost 5000 http localhost 5000 but this can be changed in config greynir conf config greynir conf web scrapers bash python scraper py if you are running the scraper on macos you may run into problems with python s fork this can be fixed by setting the following environment variable in your shell bash export objc disable initialize fork safety yes processors bash python processor py this will run all processors in the processors directory on any unprocessed articles in the database interactive shell you can launch an ipython https ipython org repl shell with a database session s the greynir parser r and all sqlalchemy database models preloaded see using the greynir shell docs shell md for instructions contributing see contributing to greynir contributing md license greynir is copyright copy 2023 mi eind ehf https mideind is the original author of this software is vilhj lmur orsteinsson a href https mideind is img src static img mideind horizontal small png alt mi eind ehf width 214 height 66 align right style margin left 20px margin bottom 20px a this set of programs is free software you can redistribute it and or modify it under the terms of the gnu general public license as published by the free software foundation either version 3 of the license or at your option any later version this set of programs is distributed in the hope that it will be useful but without any warranty without even the implied warranty of merchantability or fitness for a particular purpose see the gnu general public license for more details a href https www gnu org licenses gpl 3 0 html img src static img gplv3 png align right style margin left 15px width 180 height 60 a the full text of the gnu general public license v3 is included here https github com mideind greynir blob master license txt and also available here https www gnu org licenses gpl 3 0 html https www gnu org licenses gpl 3 0 html if you wish to use this set of programs in ways that are not covered under the gnu gplv3 license please contact us at mideind mideind is mailto mideind mideind is to negotiate a custom license this applies for instance if you want to include or use this software in part or in full in other software that is not licensed under gnu gplv3 or other compatible licenses acknowledgements greynir uses the b n beygingarl sing slensks n t mam ls https bin arnastofnun is lexicon and database of icelandic word forms to identify words and find their potential meanings and lemmas the database is included in binpackage https github com mideind binpackage in compressed form b n is licensed under cc by 4 0 and credit is hereby given as follows beygingarl sing slensks n t mam ls stofnun rna magn ssonar slenskum fr um h fundur og ritstj ri krist n bjarnad ttir the greynir web interface uses map data from openstreetmap https www openstreetmap org google https maps google com and wikipedia https is wikipedia org | python parse-trees parse-forests natural-language-processing grammar earley parser tokenizer icelandic tf-idf natural-language-queries icelandic-news-sites icelandic-language information-extraction nlp | ai |
QA4RE | llm qa4re data and code for acl 2023 findings aligning instruction tasks unlocks large language models as zero shot relation extractors https arxiv org pdf 2305 11159 pdf we present llm qa4re which aligns underrepresented tasks in the instruction tuning dataset relation extraction to a common task question answering to unlock instruction tuned llms abilities on relation extraction qa4re achieves significant and consistent performance gains over 6 llms across 4 datasets in addition it shows strong transferability to model sizes from 175b gpt 3 5 series to even 80m flan t5 small qa4re main figure jpeg https s2 loli net 2023 05 15 lk1saynjni3yqwp jpg a href https sm ms image lk1saynjni3yqwp target blank img src https s2 loli net 2023 05 15 lk1saynjni3yqwp jpg width 75 height 75 a todo x organize and release code for gpt 3 5 series llms x release output results of gpt 3 5 series llms x organize and release code for flan t5 series llms refactor code to save results as json jsonl installation run the following commands to create a conda environment with the required packages shell conda create n qa4re python 3 9 13 pip conda activate qa4re pip install r requirements txt same env with few shot bioie data and launch download data and subsets via google drive https drive google com file d 1tab7v4 bv76fipgmsoowwjnzptpeptwe view usp sharing results and prompts are saved in google drive https drive google com file d 1hsbwd6qf5nsh9w5uwgsj9snnpjultpkh view usp sharing are prepared in data dir unzip directly in and then the root folder should organize like this data retacred tacred tacrev semeval outputs retacred tacred tacrev semeval projs qa4re vanillare readme md re templates py re utils py utils for running please refer to the readme projs readme md in projs dir results qa4re works on gpt 3 5 series and flan t5 series 6 llms in total qa4re table 1 jpeg https s2 loli net 2023 05 15 is8xgo71lodm3bq jpg a href https sm ms image is8xgo71lodm3bq target blank img src https s2 loli net 2023 05 15 is8xgo71lodm3bq jpg width 75 height 75 a qa4re works on smaller instruction tuned models qa4re table 8 jpeg https s2 loli net 2023 05 15 ieurgubwn9fnmb8 jpg a href https sm ms image ieurgubwn9fnmb8 target blank img src https s2 loli net 2023 05 15 ieurgubwn9fnmb8 jpg width 75 height 75 a cite if you find our paper code or data helpful please consider citing the paper inproceedings zhang2023llm qa4re title aligning instruction tasks unlocks large language models as zero shot relation extractors author kai zhang bernal jim nez guti rrez yu su booktitle findings of acl year 2023 this work is based on our prior work inproceedings gutierrez2022thinking title thinking about gpt 3 in context learning for biomedical ie think again author bernal jim nez guti rrez nikolas mcneal clay washington you chen lang li huan sun yu su booktitle findings of emnlp year 2022 question if you have any questions please feel free to contact drogozhang at gmail dot com or open an issue so we can help you better and quicker | instruction-tuning large-language-models relation-extraction zero-shot-learning | ai |
EPDemo | epdemo demo project for engineering practices in ios workshop | os |
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omkar-shrestha | omkar shrestha information technology | server |
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fe | img src https raw githubusercontent com ethereum fe master logo fe svg fe source svg width 150px fe is an emerging smart contract language for the ethereum blockchain build status https github com ethereum fe workflows ci badge svg https github com ethereum fe actions coverage https codecov io gh ethereum fe branch master graph badge svg https codecov io gh ethereum fe note the larger part of the master branch will be replaced with the brand new implementation which is currently under development in the fe v2 https github com ethereum fe tree fe v2 branch please refer to the branch if you kindly contribute to fe overview fe is a statically typed language for the ethereum virtual machine evm it is inspired by python and rust which makes it easy to learn especially for new developers entering the ethereum ecosystem features goals bounds and overflow checking decidability by limitation of dynamic program behavior more precise gas estimation as a consequence of decidability static typing pure function support restrictions on reentrancy static looping module imports standard library usage of yul https docs soliditylang org en latest yul html ir to target both evm and ewasm wasm compiler binaries for enhanced portability and in browser compilation of fe contracts implementation in a powerful systems oriented language rust with strong safety guarantees to reduce risk of compiler bugs additional information about design goals and background can be found in the official announcement https snakecharmers ethereum org fe a new language for the ethereum ecosystem language specification we aim to provide a full language specification that should eventually be used to formally verify the correctness of the compiler a work in progress draft of the specification can be found here http fe lang org docs spec index html progress fe development is still in its early stages we have a basic roadmap for 2021 https notes ethereum org lvhatf30sjopkbg1ivw1jg that we want to follow we generally try to drive the development by working through real world use cases our next goal is to provide a working uniswap implementation in fe which will help us to advance and form the language fe had its first alpha release january 2021 and is now following a monthly release cycle getting started build the compiler https github com ethereum fe blob master docs src development build md or download the binary release https github com ethereum fe releases to compile fe code 1 run fe path to fe source fe 2 fe creates a directory output in the current working directory that contains the compiled binary and abi run fe help to explore further options examples the following is a simple contract implemented in fe fe struct signed pub book msg string 100 contract guestbook messages map address string 100 pub fn sign mut self mut ctx context book msg string 100 self messages ctx msg sender book msg ctx emit signed book msg book msg pub fn get msg self addr address string 100 return self messages addr to mem a lot more working examples can be found in our test fixtures directory https github com ethereum fe tree master crates test files fixtures demos the most advanced example that we can provide at this point is an implementation of the uniswap v2 core contracts https github com ethereum fe blob master crates test files fixtures demos uniswap fe community twitter official fe https twitter com official fe chat discord https discord gg ywpkaxfjzh license the fe implementation is split into several crates crates that depend on the solidity compiler directly or indirectly are licensed gpl 3 0 or later this includes the fe cli tool yulc driver tests and test utils the remaining crates are licensed apache 2 0 this includes the parser analyzer mir abi and common | blockchain |
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Embedded-system-design-using-MCU- | embedded system design using mcu elec291 embedded system design | os |
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awesome-holistic-3d | holistic 3d reconstruction a list of papers and resources for holistic 3d reconstruction related tutorials and workshops eccv 2020 workshop holistic scene structures for 3d vision https holistic 3d github io eccv20 iccv 2019 tutorial holistic 3d reconstruction learning to reconstruct holistic 3d structures from sensorial data https holistic 3d github io iccv19 datasets scene level datasets scenes rooms frames annotated structures planercnn https github com nvlabs planercnn 1 500 1 500 100 000 randomly sampled from 1 million planes replica https github com facebookresearch replica dataset 18 n a planes wireframe https github com huangkuns wireframe 5 462 wireframe 2d wireframe reconstruction https yichaozhou com publication 1811learning synthetic and real images wireframe 3d sun primitive http 3dvision princeton edu projects 2012 sunprimitive 785 cuboid pyramid cylinder sphere etc lsun room layout n a 5 394 cuboid layout panocontext http panocontext cs princeton edu n a 500 pano cuboid layout layoutnet https github com zouchuhang layoutnet n a 1 071 pano cuboid layout matterportlayout https github com ericsujw matterport3dlayoutannotation n a 2 295 rgb d pano manhattan layout floor sp https github com woodfrog floor sp 100 707 1500 every scene has a set of rgb d pano floorplan with non manhattan structures floornet https github com art programmer floornet 150 1000 floorplan raster to vector https github com art programmer floorplantransformation 870 floorplan 3rscan https waldjohannau github io rio 478 objects structured3d https structured3d dataset org 3 500 21 835 196 515 pritimitves points lines planes and relationships 3d object instance bounding boxes object level datasets images categories 3d models annotated structures notes keypoint 5 http 3dinterpreter csail mit edu 8 649 5 keypoints ikea keypoints http 3dinterpreter csail mit edu 759 219 keypoints derived from ikea 3d http ikea csail mit edu ansi mechanical component https github com lingxiaoli94 spfn 504 17 197 plane sphere cylinder cone etc partnet https cs stanford edu 7ekaichun partnet 24 26 671 fine grained instance level and hierarchical 3d parts derived from shapenet partnet symh https github com foggyu partnet symh 24 22 369 symmetry hierarchical 3d parts derived from partnet structurenet https cs stanford edu kaichun structurenet 6 symmetry hierarchical 3d parts derived from partnet datasets examples planercnn https github com nvlabs planercnn div align center img src figures planercnn jpg div div align center from left to right input rgb image planar segmentation depthmap div wireframe https github com huangkuns wireframe div align center img src figures wireframe jpg div div align center first row manually labelled line segments second row groundtruth junctions div wireframe reconstruction https yichaozhou com publication 1811learning div align center img src figures wireframe reconstruction jpg div div align center from left column to right column input image with groundtruth wireframes predicted 3d wireframe and alternative view of the same image div sun primitive http 3dvision princeton edu projects 2012 sunprimitive div align center img src figures sun primitive jpg div div align center yellow groundtruth green correct detection red false alarm div lsun room layout div align center img src figures lsun room png div div align center from left right input rgb image room layout corner representation room layout segmentation representation div panocontext http panocontext cs princeton edu div align center img src figures panocontext jpg div div align center from left to right a single view panorama object detection and 3d reconstruction div layoutnet https github com zouchuhang layoutnet div align center img src figures layoutnet jpg div div align center orange lines predicted layout green lines groundtruth layout div raster to vector https github com art programmer floorplantransformation div align center img src figures raster to vector jpg div div align center from left to right an input floorplan image reconstructed vector graphics representation visualized by custom renderer and a popup 3d model div floor sp https github com woodfrog floor sp div align center img src figures floor sp png div div align center from left to right stitched rgb d panorama of indoor scenes top view point density normal map vector graphics floorplan with non manhattan structures div structured3d https structured3d dataset org div align center img src figures structured3d jpg div div align center a house designs b ground truth 3d structure annotations c photo realistic 2d images div keypoint 5 http 3dinterpreter csail mit edu and ikea keypoints http 3dinterpreter csail mit edu div align center img src figures keypoints 5 input jpeg img src figures keypoints 5 label jpeg div div align center left input image right labeled 2d keypoints div ansi mechanical component https github com lingxiaoli94 spfn div align center img src figures ansi jpg div div align center up to down input point cloud and geometric primitives div partnet https cs stanford edu 7ekaichun partnet div align center img src figures partnet jpg div div align center from left column to right column three levels from coarse to fine grained of segmentation annotations in the hierarchy for three segmentation tasks div partnet symh https github com foggyu partnet symh div align center img src figures partnet symh jpg div div align center odd rows groundtruth fine grained segmentation results even rows prediction fine grained segmentation results div references books y ma s soatto j kosecka and s s sastry an invitation to 3d vision from images to geometric models springer verlag 2003 r i hartley and a zisserman multiple view geometry in computer vision cambridge university press 2000 papers scene level 2020 y nie x han s guo y zheng j chang and j j zhang total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image in cvpr 2020 project https yinyunie github io total3d z jiang b liu s schulter z wang and m chandraker peek a boo occlusion reasoning in indoor scenes with plane representations in cvpr 2020 paper https openaccess thecvf com content cvpr 2020 html jiang peek a boo occlusion reasoning in indoor scenes with plane representations cvpr 2020 paper html h zeng k joseph a vest and y furukawa bundle pooling for polygonal architecture segmentation problem in cvpr 2020 paper https openaccess thecvf com content cvpr 2020 html zeng bundle pooling for polygonal architecture segmentation problem cvpr 2020 paper html f zhang n nauata and y furukawa conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction in cvpr 2020 project https ennauata github io buildings2vec page html j zheng j zhang j li r tang s gao and z zhou structured3d a large photo realistic dataset for structured 3d modeling in eccv 2020 project https structured3d dataset org y qian and y furukawa learning inter plane relations for piecewise planar reconstruction in eccv 2020 qian shengyi linyi jin and david f fouhey associative3d volumetric reconstruction from sparse views in eccv 2020 project https jasonqsy github io associative3d pintore giovanni marco agus and enrico gobbetti atlantanet inferring the 3d indoor layout from a single 360 image beyond the manhattan world assumption in eccv 2020 project https www crs4 it vic cgi bin bib page cgi id 27pintore 2020 ai3 27 avetisyan armen et al scenecad predicting object alignments and layouts in rgb d scans in eccv 2020 paper https arxiv org pdf 2003 12622 pdf lin yancong silvia l pintea and jan c van gemert deep hough transform line priors in eccv 2020 project https github com yanconglin deep hough transform line priors 2019 y zhou h qi and y ma neurvps neural vanishing point scanning via conic convolution in neurips 2019 project https github com zhou13 neurvps y zhou h qi and y ma end to end wireframe parsing in iccv 2019 project https github com zhou13 lcnn y zhou h qi y zhai q sun z chen l wei and y ma learning to reconstruct 3d manhattan wireframes from a single image in iccv 2019 project https arxiv org abs 1905 07482 j chen c liu j wu and y furukawa floor sp inverse cad for floorplans by sequential room wise shortest path in iccv 2019 project https github com woodfrog floor sp j wald a avetisyan n navab f tombari and m niessner rio 3d object instance re localization in changing indoor environments in iccv 2019 project https waldjohannau github io rio c zou j w su c h peng a colburn q shan p wonka h k chu and d hoiem 3d manhattan room layout reconstruction from a single 360 image 2019 arxiv 1910 04099 2019 project https github com ericsujw matterport3dlayoutannotation c liu k kim j gu y furukawa and j kautz planercnn 3d plane detection and reconstruction from a single image in cvpr 2019 project https research nvidia com publication 2019 06 planercnn z yu j zheng d lian z zhou and s gao single image piece wise planar 3d reconstruction via associative embedding in cvpr 2019 project https github com svip lab planarreconstruction z zhang z li n bi j zheng j wang k huang w luo y xu and s gao ppgnet learning point pair graph for line segment detection in cvpr 2019 project https github com svip lab ppgnet 2018 f yang and z zhou recovering 3d planes from a single image via convolutional neural networks in eccv 2018 project https github com fuy34 planerecover h zeng j wu and y furukawa neural procedural reconstruction for residential buildings in eccv 2018 paper http openaccess thecvf com content eccv 2018 papers huayi zeng neural procedural reconstruction eccv 2018 paper pdf c liu j yu and y furukawa floornet a unified framework for floorplan reconstruction from 3d scans in eccv 2018 project https github com art programmer floornet c zou a colburn q shan and d hoiem layoutnet reconstructing the 3d room layout from a single rgb image in cvpr 2018 project https github com zouchuhang layoutnet c liu j yang d ceylan e yumer and y furukawa planenet piece wise planar reconstruction from a single rgb image in cvpr 2018 project https github com art programmer planenet k huang y wang z zhou t ding s gao and y ma learning to parse wireframes in images of man made environments in cvpr 2018 project https github com huangkuns wireframe h fang f lafarge and m desbrun planar shape detection at structural scales in cvpr 2018 paper http openaccess thecvf com content cvpr 2018 html fang planar shape detection cvpr 2018 paper html 2017 l nan and p wonka polyfit polygonal surface reconstruction from point clouds in iccv 2017 project https github com liangliangnan polyfit c liu j wu p kohli and y furukawa raster to vector revisiting floorplan transformation in iccv 2017 project https github com art programmer floorplantransformation c lee v badrinarayanan t malisiewicz and a rabinovich roomnet end to end room layout estimation in iccv 2017 paper https arxiv org abs 1703 06241 h izadinia q shan and s m seitz im2cad in cvpr 2017 project https homes cs washington edu izadinia im2cad html e wijmans and y furukawa exploiting 2d floorplan for building scale panorama rgbd alignment in cvpr 2017 project https cvpr17 wijmans xyz t kelly j femiani p wonka and n mitra bigsur large scale structured urban reconstruction in siggraph asia 2017 paper https geometry cs ucl ac uk projects 2017 bigsur paper docs paper pdf 2016 m li p wonka and l nan manhattan world urban reconstruction from point clouds in eccv 2016 paper https 3d bk tudelft nl liangliang publications 2016 manhattan manhattan eccv2016 pdf c zhu z zhou z xing y dong y ma and j yu robust plane based calibration of multiple non overlapping cameras in 3dv 2016 paper https faculty ist psu edu zzhou paper 3dv16 calibration pdf c liu p kohli and y furukawa layered scene decomposition via the occlusion crf in cvpr 2016 project https sites wustl edu chenliu layered scene s dasgupta k fang k chen and s savarese delay robust spatial layout estimation for cluttered indoor scenes in cvpr 2016 paper http cvgl stanford edu papers delay robust spatial pdf s oesau f lafarge and p alliez planar shape detection and regularization in tandem computer graphics forum 2016 paper https hal inria fr hal 01168394 document 2015 s ikehata h yan and y furukawa structured indoor modeling in iccv 2015 paper https www cv foundation org openaccess content iccv 2015 papers ikehata structured indoor modeling iccv 2015 paper pdf o haines and a calway recognising planes in a single image ieee tpami 2015 paper https ieeexplore ieee org stamp stamp jsp arnumber 6987324 a monszpart n mellado g j brostow and n j mitra rapter rebuilding man made scenes with regular arrangements of planes in siggraph 2015 paper https geometry cs ucl ac uk projects 2015 regular arrangements of planes paper docs rapter monszpartetal siggraph 2015 pdf j favreau f lafarge and a bousseau line drawing interpretation in a multi view context in cvpr 2015 2014 d f fouhey a gupta and m hebert unfolding an indoor origami world in eccv 2014 paper https ri cmu edu pub files 2014 7 dfouhey origami pdf r cabral and y furukawa piecewise planar and compact floorplan reconstruction from images in cvpr 2014 paper https www andrew cmu edu user rcabral cabralfurukawacvpr2014 pdf d ceylan n j mitra y zheng m pauly coupled structure from motion and 3d symmetry detection for urban facades acm transactions on graphics 2014 paper http vecg cs ucl ac uk projects smartgeometry symm facade paper docs symmcalibrate reduced pdf 2013 s ramalingam and m brand lifting 3d manhattan lines from a single image in iccv 2013 paper https www merl com publications docs tr2013 110 pdf s ramalingam j k pillai a jain and y taguchi manhattan junction catalogue for spatial reasoning of indoor scenes in cvpr 2013 paper https ieeexplore ieee org document 6619238 z zhou h jin and y ma plane based content preserving warps for video stabilization in cvpr 2013 paper https www cv foundation org openaccess content cvpr 2013 papers zhou plane based content preserving 2013 cvpr paper pdf n j mitra m pauly m wand and d ceylan symmetry in 3d geometry extraction and applications computer graphics forum 2013 paper https englishformaths files wordpress com 2016 02 symmetrystar small eg12 pdf 2012 j xiao b c russell and a torralba localizing 3d cuboids in single view images in nips 2012 paper http papers nips cc paper 4842 localizing 3d cuboids in single view images pdf j xiao and y furukawa reconstructing the world s museums in eccv 2012 paper https vision princeton edu projects 2012 museum paper pdf z zhou h jin and y ma robust plane based structure from motion in cvpr 2012 paper https faculty ist psu edu zzhou paper 1442 pdf a cohen c zach s n sinha and m pollefeys discovering and exploiting 3d symmetries in structure from motion in cvpr 2012 paper https snsinha github io pdfs cohencvpr2012 pdf c a vanegas d g aliaga and b benes automatic extraction of manhattan world building masses from 3d laser range scans ieee tvcg 2012 paper https ieeexplore ieee org document 6143940 2011 h mobahi z zhou a y yang and y ma holistic reconstruction of urban structures from low rank textures in iccv 3drr 2011 paper https ieeexplore ieee org abstract document 6130297 z zhang x liang and y ma unwrapping low rank textures on generalized cylindrical surfaces in iccv 2011 paper https ieeexplore ieee org document 6126388 a flint d w murray and i reid manhattan scene understanding using monocular stereo and 3d features in iccv 2011 paper https ieeexplore ieee org document 6126501 c wu j m frahm and m pollefeys repetition based dense single view reconstruction in cvpr 2011 paper https inf ethz ch personal pomarc pubs wucvpr11 pdf a elqursh and a m elgammal line based relative pose estimation in cvpr 2011 paper https www cs rutgers edu elgammal pub cvpr11 elqursh pdf z zhang y matsushita and y ma camera calibration with lens distortion from low rank textures in cvpr 2011 paper http people csail mit edu zhangzd papers calibration cvpr11 pdf 2010 and before d gallup j m frahm and m pollefeys piecewise planar and non planar stereo for urban scene reconstruction in cvpr 2010 paper https ieeexplore ieee org document 5539804 y furukawa b curless s m seitz and r szeliski reconstructing building interiors from images in iccv 2009 paper http grail cs washington edu wp content uploads 2016 09 furukawa2009rbi pdf v hedau d hoiem and d a forsyth recovering the spatial layout of cluttered rooms in iccv 2009 paper http dhoiem cs illinois edu publications iccv2009 hedau indoor pdf y furukawa b curless s m seitz and r szeliski manhattan world stereo in cvpr 2009 paper http grail cs washington edu projects manhattan manhattan pdf d c lee m hebert and t kanade geometric reasoning for single image structure recovery in cvpr 2009 paper https www ri cmu edu pub files 2009 6 cvpr09lee pdf g schindler p krishnamurthy r lublinerman y liu and f dellaert detecting and matching repeated patterns for automatic geo tagging in urban environments in cvpr 2008 paper https www ri cmu edu pub files pub4 schindler grant 2008 1 schindler grant 2008 1 pdf b micusik h wildenauer and j kosecka detection and matching of rectilinear structures in cvpr 2008 paper https ieeexplore ieee org document 4587488 d hoiem a a efros and m hebert recovering surface layout from an image ijcv 2007 paper http dhoiem cs illinois edu publications hoiem ijcv2007surfacelayout pdf g schindler p krishnamurthy and f dellaert line based structure from motion for urban environments in 3dpvt 2006 paper https www cc gatech edu projects 4d cities dhtml pubs schindler06 3dpvt pdf j m coughlan and a l yuille manhattan world orientation and outlier detection by bayesian inference neural computation 2003 paper https ieeexplore ieee org document 6790299 a bartoli and p sturm constrained structure and motion from multiple uncalibrated views of a piecewise planar scene ijcv 2003 paper https link springer com article 10 1023 a 1022318524906 j kosecka and w zhang video compass in eccv 2002 paper https link springer com chapter 10 1007 3 540 47979 1 32 a p witkin and j m tenenbaum on the role of structure in vision in j beck b hope and a rosenfeld editors human and machine vision pages 481 543 academic press 1983 paper http cvcl mit edu sunseminar barenholtztarr 2007 pdf papers object level 2020 m gadelha g gori d ceylan r m ch m carr t boubekeur r wang s maji learning generative models of shape handles in cvpr 2020 xu yifan et al ladybird quasi monte carlo sampling for deep implicit field based 3d reconstruction with symmetry in eccv 2020 paper https arxiv org abs 1811 08988 lin cheng et al modeling 3d shapes by reinforcement learning in eccv 2020 paper https arxiv org abs 2003 12397 li yichen et al learning 3d part assembly from a single image in eccv 2020 project https cs stanford edu kaichun impartass zhang zaiwei et al h3dnet 3d object detection using hybrid geometric primitives in eccv 2020 project https github com zaiweizhang h3dnet 2019 k mo p guerrero l yi h su p wonka n j mitra and l guibas structurenet hierarchical graph networks for 3d shape generation in siggraph asia 2019 project https cs stanford edu kaichun structurenet c sun q zou x tong and y liu learning adaptive hierarchical cuboid abstractions of 3d shape collections in siggraph asia 2019 project https isunchy github io projects cuboid abstraction html d paschalidou a o ulusoy and a geiger superquadrics revisited learning 3d shape parsing beyond cuboids in cvpr 2019 paper https arxiv org abs 1904 09970 l li m sung a dubrovina l yi and l guibas supervised fitting of geometric primitives to 3d point clouds in cvpr 2019 project https github com fuxicv ladybird 2018 j wu t xue j j lim y tian j b tenenbaum a torralba and w t freeman 3d interpreter networks for viewer centered wireframe modeling ijcv 2018 project http 3dinterpreter csail mit edu c niu j li and k xu im2struct recovering 3d shape structure from a single rgb image in cvpr 2018 project https github com chengjieniu im2struct 2017 c zou e yumer j yang d ceylan and d hoiem 3d prnn generating shape primitives with recurrent neural networks in iccv 2017 paper http openaccess thecvf com content iccv 2017 papers zou 3d prnn generating shape iccv 2017 paper pdf s tulsiani h su l j guibas a a efros and j malik learning shape abstractions by assembling volumetric primitives in cvpr 2017 project https shubhtuls github io volumetricprimitives 2013 l yang j liu and x tang complex 3d general object reconstruction from line drawings in iccv 2013 paper https www cv foundation org openaccess content iccv 2013 papers yang complex 3d general 2013 iccv paper pdf n j mitra m wand h zhang d cohen or and m bokeloh structure aware shape processing in eurographics 2013 project http vecg cs ucl ac uk projects smartgeometry structure survey structuresurvey eg13 html 2012 s n sinha k ramnath and r szeliski detecting and reconstructing 3d mirror symmetric objects in eccv 2012 paper https snsinha github io pdfs sinhaeccv2012 pdf t xue y li j liu and x tang example based 3d object reconstruction from line drawings in cvpr 2012 paper http mmlab ie cuhk edu hk pdf xuecvpr12 pdf 2011 y li x wu y chrysathou a sharf d cohen or and n j mitra globfit consistently fitting primitives by discovering global relations in siggraph 2011 project http vecg cs ucl ac uk projects smartgeometry globfit globfit sigg11 html 2010 and before t xue j liu and x tang object cut complex 3d object reconstruction through line drawing separation in cvpr 2010 paper http mmlab ie cuhk edu hk pdf xuecvpr10 pdf m bokeloh a berner m wand h p seidel and a schilling symmetry detection using feature lines in eurographics 2009 paper https www cs princeton edu courses archive spring11 cos598a pdfs bokeloh09 pdf l cao j liu and x tang what the back of the object looks like 3d reconstruction from line drawings without hidden lines pami 2008 paper http mmlab ie cuhk edu hk pdf caotpami08 pdf m pauly n j mitra j wallner h pottmann and l j guibas discovering structural regularity in 3d geometry in siggraph 2008 paper https lgg epfl ch publications 2008 pauly 2008 dsr pdf y wang y chen j liu and x tang 3d reconstruction of curved objects from single 2d line drawings in cvpr 2008 paper http mmlab ie cuhk edu hk pdf yzwangcvpr08 pdf y chen j liu and x tang a divide and conquer approach to 3d object reconstruction from line drawings in iccv 2007 paper http mmlab ie cuhk edu hk pdf cheniccv07 pdf n j mitra l guibas and m pauly partial and approximate symmetry detection for 3d geometry acm tog 2006 paper j malik interpreting line drawings of curved objects ijcv 1987 paper https people eecs berkeley edu malik papers malik87 pdf k sugihara mathematical structures of line drawings of polyhedrons toward man machine communication by means of line drawings ieee tpami 1982 paper https ieeexplore ieee org document 4767289 | awesome 3d-reconstruction computer-vision deep-learning machine-learning datasets | ai |
practical-computer-vision-applications | practical computer vision applications source code for practical computer vision applications using deep learning with cnns by ahmed fawzy mohamed gad the book is available at amazon here https www amazon com practical computer vision applications learning dp 1484241665 9781484241660 https user images githubusercontent com 16560492 59639499 bd91a100 915b 11e9 84c8 eb0a7acfd20d jpg | ai |
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lets-build-a-blockchain | let s build a blockchain a mini cryptocurrency in ruby https i ytimg com vi 3aji1abdjqk hqdefault jpg video of the talk https youtu be 3aji1abdjqk t 17m23s slides here https speakerdeck com haseebq lets build a blockchain a mini cryptocurrency in ruby | ruby blockchain cryptocurrency bitcoin sinatra | blockchain |
koan | koan client images koan png build status https travis ci org soygul koan svg branch master https travis ci org soygul koan koan stack is a boilerplate that provides a starting point for full stack javascript web development with koa https github com koajs koa angularjs http angularjs org and node js http www nodejs org along with mongodb https www mongodb org and websockets https developer mozilla org en docs websockets a summary of tech stack client angularjs and bootstrap client side is fully static and cdn ready all client packages are located at client bower packages server koa for restful api serving on node js es6 async await functions all the way websockets along with json rpc is used for real time client server communication and browser sync oauth 2 is used for social authentications instead of auth cookies we use jwt along with html5 local storage grunt tasks are used to facilitate development and testing mongodb for persistence live example below are the screenshots from the bundled demo app which is a facebook like real time sharing app follow instructions in the next section if you want to play with it locally or deploy it to heroku login page login page client images scrshot login png user home page home page client images scrshot home png getting started make sure that you have node js v7 6 or higher for node 7 6 use v1 6 release https github com soygul koan releases tag v1 6 and mongodb v2 or higher running on the default port 27017 installed on your computer to get started with koan stack do following bash git clone depth 1 https github com soygul koan git cd koan npm install npm start your application should run on the 3000 port so in your browser just go to http localhost 3000 http localhost 3000 if you want to run tests simply type bash npm test configuration all configuration is specified in the server config server config directory particularly the config js server config config js file here you can hook up any social app keys if you want integration with twitter facebook or google heroku deployment deploy https www herokucdn com deploy button svg https heroku com deploy procfile and app json are making this repo readily available for heroku deployment you can start by clicking the above button testing you can run all the tests with npm test tests are run with client unit jasmine karma angular default client e2e jasmine protractor angular default server mocha supertest should koa default see test server users js test server users js for an example credits client side is entirely based on the official angular seed https github com angular angular seed server side simply utilizes generally accepted koa middleware and node js best practices the name the project name is an acronym for koa angular and node it also is the name for a zen buddhist riddle used to focus the mind during meditation and to develop intuitive thinking license mit | front_end |
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autofix | autofix static analysis llm autofix note if you are looking for a cloud service for vulnerability remediation please try patched https www patched codes update on 21th aug 2023 the new starcoder https huggingface co bigcode starcoderbase 1b model is now supported pass model bigcode starcoderbase 1b to autofix to try the 1b parameter base model update on 4th may 2023 we now support using the codegen2 https github com salesforce codegen2 model from salesforce just use model salesforce codegen2 1b with autofix note that the inference on cpu with codegen2 is very slow compared to santafixer in the initial release we used semgrep for doing static analysis and the santafixer https huggingface co lambdasec santafixer llm for bug fixing setup python3 m venv venv source venv bin activate pip install r requirements txt usage python autofix py input examples example java demo https github com lambdasec autofix blob main demo gif how it works https github com lambdasec autofix blob main howitworks png | auto-remediation bug-fixing large-language-models remediation static-analysis vulnerability-detection | ai |
blockchain-for-software-engineers | blockchain for software engineers inspired by google interview university https www google com machine learning for software engineers https github com zuzoovn machine learning for software engineers the authoritative guide to blockchain development https medium freecodecamp org the authoritative guide to blockchain development 855ab65b58bc the blockchain symbolizes a shift in power from the centers to the edges of the networks william mougayar what is it a collection of resources that i ve gathered over the past couple years while advancing in the blockchain world hope this can help others this list is by no means exhaustive and is centered on the end goal of building production quality smart contracts and dapps my journey has been focused on ethereum but i would love this list to expand and not just be limited to one blockchain all contributions welcome about me security researcher and engineer goal create and curate a list of resources for aspiring and seasoned blockchain engineers table of contents what is it what is it about me about me goal goal covering the material covering the material how to use it how to use it data structures data structures cryptography cryptography distributed systems distributed systems networking networking economics economics intro to bitcoin and blockchain intro to bitcoin and blockchain bitcoin and blockchain tutorials bitcoin and blockchain tutorials advanced bitcoin advanced bitcoin bitcoin and blockchain books bitcoin and blockchain books intro to ethereum intro to ethereum solidity and smart contracts solidity and smart contracts beginner ethereum tutorials beginner ethereum tutorials smart contract security smart contract security tools of the trade tools of the trade ethereum tutorials ethereum tutorials ethereum books ethereum books blockchain blogs blockchain blogs forums and threads forums and threads other tools other tools podcasts podcasts hackathons hackathons additional curated lists of resources additional curated lists of resources getting a job getting a job job boards job boards covering the material over the past two years i found it useful to pick a subject from the list below read it thoroughly take notes and implement on a blockchain running locally i tend to prefer a top down hands on approach to learning i found it extremely important to build and push my understanding further daily how to use it everything below is a prioritized outline and you should work from the top to the bottom x create a new branch so you can check items like this just put an x in the brackets x background blockchain technology is a multi disciplinary field built atop cryptography economics and computer science the background section includes some requisite fundamentals in these fields that will ensure you have solid footing before diving deeper into the blockchain specifics since many of the features that exist today are results of years and years of previous research data structures linked lists https en wikipedia org wiki linked list binary search trees https en wikipedia org wiki binary search tree hash maps https en wikipedia org wiki hash table graphs https en wikipedia org wiki graph 28discrete mathematics 29 directed acyclic graphs https en wikipedia org wiki directed acyclic graph cryptography public private key cryptography https en wikipedia org wiki public key cryptography rsa https www youtube com watch v vgtthv04xri ecdsa https en wikipedia org wiki elliptic curve digital signature algorithm cryptographic hash function https en wikipedia org wiki cryptographic hash function commitment schemes https en wikipedia org wiki commitment scheme merkle trees https en wikipedia org wiki merkle tree merkle proofs https indigocore org documentation v0 1 0 references proof of existence distributed systems consistency https en wikipedia org wiki consistency model consensus https en wikipedia org wiki consensus 28computer science 29 linearizable https en wikipedia org wiki linearizability eventual consistency https en wikipedia org wiki eventual consistency fault tolerance https en wikipedia org wiki fault tolerance paxos https en wikipedia org wiki paxos 28computer science 29 raft https en wikipedia org wiki raft 28computer science 29 time in a distributed system https www youtube com watch v brvj8pyksc4 tradeoff safety vs liveness http www bailis org blog safety and liveness eventual consistency is not safe byzantine fault tolerance https en wikipedia org wiki byzantine fault tolerance pbft https blog acolyer org 2015 05 18 practical byzantine fault tolerance distributed databases https en wikipedia org wiki distributed database sharding https en wikipedia org wiki shard 28database architecture 29 consistent hashing https en wikipedia org wiki consistent hashing leader follower replication https en wikipedia org wiki replication 28computing 29 quorum based commits https en wikipedia org wiki quorum 28distributed computing 29 distributed hash tables https en wikipedia org wiki distributed hash table chord https en wikipedia org wiki chord 28peer to peer 29 kademilia https en wikipedia org wiki kademlia networking computer networking https en wikipedia org wiki computer network tcp vs udp https www diffen com difference tcp vs udp the packet model https en wikipedia org wiki network packet internet routing https en wikipedia org wiki routing gossip protocols https en wikipedia org wiki gossip protocol flooding https en wikipedia org wiki flooding 28computer networking 29 p2p network design https en wikipedia org wiki peer to peer bittorrent https en wikipedia org wiki bittorrent tor https en wikipedia org wiki tor 28anonymity network 29 economics game theory https en wikipedia org wiki game theory nash equilibria https en wikipedia org wiki nash equilibrium schelling point https en wikipedia org wiki focal point 28game theory 29 macroeconomics https en wikipedia org wiki macroeconomics monetary policy https en wikipedia org wiki monetary policy inflation https en wikipedia org wiki inflation deflation https en wikipedia org wiki deflation velocity of money https en wikipedia org wiki velocity of money microeconomics https en wikipedia org wiki microeconomics supply and demand curves https en wikipedia org wiki supply and demand opportunity cost https en wikipedia org wiki opportunity cost auction theory https www youtube com watch v 4kwuxfvbiau bitcoin intro to bitcoin and blockchain blockchain 101 https youtu be 160omzbly8 the blockchain explained to web developers https marmelab com blog 2016 04 28 blockchain for web developers the theory html what is a distributed ledger https www coindesk com information what is a distributed ledger how bitcoin actually works https youtu be bbc nxj3ng4 bitcoin whitepaper https bitcoin org bitcoin pdf bitcoin mining https www youtube com watch v jxerv3f5jn8 walk through a bitcoin block header https www youtube com watch v guwxct1qkbu bitcoin block explorer https blockchain info bitcoin history https youtu be apyieuvnuae bitcoin forum https bitcointalk org index php board 1 0 blockchains from a distributed computing perspective http cs brown edu courses csci2952 a papers perspective pdf bitcoin and blockchain tutorials build your own blockchain https youtu be 3aji1abdjqk review blockchain projects https github com openblockchains awesome blockchains advanced bitcoin bitcoin s academic pedigree https queue acm org detail cfm id 3136559 bitcoin forks https www coindesk com short guide bitcoin forks explained double spends 51 attacks https www youtube com watch v upxacj8zseu replay attacks https bitcointechtalk com how to protect against replay attacks 7a00bd2fe52f gi 618160cafff4 segregated witness https en wikipedia org wiki segwit bitcoin scalability problems https en wikipedia org wiki bitcoin scalability problem mega bitcoin resources list https lopp net bitcoin html books mastering bitcoin https www amazon com mastering bitcoin unlocking digital cryptocurrencies dp 1449374042 blockchain blueprint for a new economy https www amazon com blockchain blueprint economy melanie swan dp 1491920491 cryptoassets the innovative investors guide to bitcoin and beyond https www amazon com cryptoassets innovative investors bitcoin beyond dp 1260026671 the business blockchain https www amazon com business blockchain practice application technology dp 1536663468 blockchain revolution https www amazon com blockchain revolution technology changing business dp 151135769x ethereum intro to ethereum ethereum whitepaper https github com ethereum wiki wiki white paper ethereum yellow paper https ethereum github io yellowpaper paper pdf ethereum in 25 minutes https youtu be mczydlana7s intro to crypto economics https www youtube com watch v sbd4xe9ohjg ethereum s account model https ethereum stackexchange com questions 326 what are the pros and cons of ethereum balances vs utxos industry support https entethalliance org members the ethreum community forum https forum ethereum org erc 20 https medium com james 3093 ethereum erc20 tokens explained 9f7f304055df the anatomy of erc20 https medium com blockchannel the anatomy of erc20 c9e5c5ff1d02 the anatomy of erc721 https medium com crypto currently the anatomy of erc721 e9db77abfc24 ethereum for web developers https medium com mvmurthy ethereum for web developers 890be23d1d0c ethereum wallets https cointelegraph com ethereum for beginners ethereum wallets a crash course for mechanism design https medium com blockchannel a crash course in mechanism design for cryptoeconomic applications a9f06ab6a976 solidity and smart contracts solidity docs http solidity readthedocs io en v0 4 20 basics of solidity by example https www toshblocks com solidity basics solidity example learn x in y https learnxinyminutes com docs solidity browser ide https remix ethereum org bitdegree https solidity bitdegree org the hitchhiker s guide to smart contracts https blog zeppelin solutions the hitchhikers guide to smart contracts in ethereum 848f08001f05 11 best ethereum development tools https hackernoon com 11 best ethereum development tools to grow your stack e782fd7156ab understanding ethereum smart contract storage https programtheblockchain com posts 2018 03 09 understanding ethereum smart contract storage beginner tutorials build your first blockchain application in 5 quick steps https itnext io build your first blockchain application in 5 quick steps 89ebb96adbfe cryptozombies https cryptozombies io build your own ethereum token https enlight nyc ethereum token building a voting system https karl tech learning solidity part 2 voting smart contract security smart contract best practices https consensys github io smart contract best practices the dao hack http hackingdistributed com 2016 06 18 analysis of the dao exploit parity wallet hack https medium freecodecamp org a hacker stole 31m of ether how it happened and what it means for ethereum 9e5dc29e33ce parity wallet hack ii https hackernoon com parity wallet hack 2 electric boogaloo e493f2365303 secure generation of randomness http www swende se blog breaking the house html ethernaut smart contract hacking game https ethernaut zeppelin solutions hack this contract game http hackthiscontract io additional resources https consensys github io smart contract best practices bibliography slither static analyzer for solidity https github com trailofbits slither tools of the trade truffle docs http truffleframework com docs ganache http truffleframework com docs ganache using ipfs docs https ipfs io docs geth https github com ethereum go ethereum wiki geth security tools slither solidity static text analyzer https github com crytic slither echidna smart contract fuzzer https github com crytic echidna mythril dynamic analysis swiss army knife https github com consensys mythril ethereum tutorials end to end ethereum tutorial https medium com merunasgrincalaitis the ultimate end to end tutorial to create and deploy a fully descentralized dapp in ethereum 18f0cf6d7e0e introduction to ethereum smart contract development https www youtube com watch v 8ji1tueatro ethereum programming for web developers https happyfuncorp com whitepapers webthereum ethereum petshop http truffleframework com tutorials pet shop how to create your own token in 1 hour https steemit com ethereum maxnachamkin how to create your own ethereum token in an hour erc20 verified how launch your own production ready cryptocurrency https hackernoon com how to launch your own production ready cryptocurrency ab97cb773371 how to create your own crowdsale https www ethereum org crowdsale building battle tested contract w open zeppelin http truffleframework com tutorials robust smart contracts with openzeppelin books mastering ethereum https github com ethereumbook ethereumbook the business blockchain https www amazon com business blockchain practice application technology dp 1536663468 mastering ethereum https www amazon com mastering ethereum building smart contracts dp 1491971940 introducing ethereum and solidity https www apress com us book 9781484225349 ethereum development with go https goethereumbook org blockchain community blogs coindesk https www coindesk com eth research https ethresear ch vitalik buterin https vitalik ca hacking distributed http hackingdistributed com unenumerated http unenumerated blogspot com money stuff https www bloomberg com view topics money stuff chris burniske https medium com cburniske great wall of numbers http www ofnumbers com forums and threads r ethdev https reddit com r ethdev r ethereum https reddit com r ethereum r cryptocurrency https www reddit com r cryptocurrency bitcoin talk https bitcointalk org other tools cryptopanic news feed https cryptopanic com crypto price screener https www cryptocompare com coins list usd 1 podcasts unchained podcast https itunes apple com us podcast unchained big ideas from worlds blockchain cryptocurrency id1123922160 mt 2 epicenter podcast https itunes apple com us podcast epicenter podcast on blockchain ethereum bitcoin distributed id792338939 mt 2 conspiratus podcast https itunes apple com us podcast conspiratus id1335928646 mt 2 hackathons hackathon com https www hackathon com theme blockchain additional curated lists of resources cryptominded https cryptominded com getting a job how to get a job at a crypto startup https angel co talent hacks how to get a job at a crypto startup job boards angellist crypto startups https angel co bitcoin jobs blockchainjobz http blockchainjobz com ethereum jobs https jobs ethercasts com be in crypto https beincrypto com blockchain job board http www blockchainjobboard org | blockchain |
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gobot | gobot https raw githubusercontent com hybridgroup gobot site master source images elements gobot logo small png http gobot io godoc https godoc org gobot io x gobot v2 status svg https godoc org gobot io x gobot v2 circleci build status https circleci com gh hybridgroup gobot tree dev svg style svg https circleci com gh hybridgroup gobot tree dev appveyor build status https ci appveyor com api projects status ix29evnbdrhkr7ud branch dev svg true https ci appveyor com project deadprogram gobot branch dev codecov https codecov io gh hybridgroup gobot branch dev graph badge svg https codecov io gh hybridgroup gobot go report card https goreportcard com badge hybridgroup gobot https goreportcard com report hybridgroup gobot license https img shields io badge license apache 202 0 blue svg https github com hybridgroup gobot blob master license txt gobot https gobot io is a framework using the go programming language https golang org for robotics physical computing and the internet of things it provides a simple yet powerful way to create solutions that incorporate multiple different hardware devices at the same time want to run go directly on microcontrollers check out our sister project tinygo https tinygo org getting started get in touch get the gobot source code by running this commands sh git clone https github com hybridgroup gobot git git checkout release afterwards have a look at the examples directory examples you need to find an example matching your platform for your first test e g raspi blink go than build the binary cross compile transfer it to your target and run it env goos linux goarch arm goarm 5 go build o output my raspi bink examples raspi blink go building the code on your local machine with the example code above will create a binary for armv5 this is probably not what you need for your specific target platform please read also the platform specific documentation in the platform subfolders create your first project create a new folder and a new go module project sh mkdir my gobot example cd my gobot example go mod init my gobot example com copy your example file besides the go mod file import the requirements and build sh cp path to gobot folder examples raspi blink go my gobot example go mod tidy env goos linux goarch arm goarm 5 go build o output my raspi bink raspi blink go now you are ready to modify the example and test your changes start by removing the build directives at the beginning of the file examples gobot with arduino go package main import time gobot io x gobot v2 gobot io x gobot v2 drivers gpio gobot io x gobot v2 platforms firmata func main firmataadaptor firmata newadaptor dev ttyacm0 led gpio newleddriver firmataadaptor 13 work func gobot every 1 time second func led toggle robot gobot newrobot bot gobot connection firmataadaptor gobot device led work robot start gobot with sphero go package main import fmt time gobot io x gobot v2 gobot io x gobot v2 platforms sphero func main adaptor sphero newadaptor dev rfcomm0 driver sphero newspherodriver adaptor work func gobot every 3 time second func driver roll 30 uint16 gobot rand 360 robot gobot newrobot sphero gobot connection adaptor gobot device driver work robot start metal gobot you can use the entire gobot framework as shown in the examples above classic gobot or you can pick and choose from the various gobot packages to control hardware with nothing but pure idiomatic golang code metal gobot for example go package main import gobot io x gobot v2 drivers gpio gobot io x gobot v2 platforms intel iot edison time func main e edison newadaptor e connect led gpio newleddriver e 13 led start for led toggle time sleep 1000 time millisecond master gobot you can also use the full capabilities of the framework aka master gobot to control swarms of robots or other features such as the built in api server for example go package main import fmt time gobot io x gobot v2 gobot io x gobot v2 api gobot io x gobot v2 platforms sphero func newswarmbot port string gobot robot spheroadaptor sphero newadaptor port spherodriver sphero newspherodriver spheroadaptor spherodriver setname sphero port work func spherodriver stop spherodriver on sphero collision func data interface fmt println collision detected gobot every 1 time second func spherodriver roll 100 uint16 gobot rand 360 gobot every 3 time second func spherodriver setrgb uint8 gobot rand 255 uint8 gobot rand 255 uint8 gobot rand 255 robot gobot newrobot sphero gobot connection spheroadaptor gobot device spherodriver work return robot func main master gobot newmaster api newapi master start spheros string dev rfcomm0 dev rfcomm1 dev rfcomm2 dev rfcomm3 for port range spheros master addrobot newswarmbot port master start hardware support gobot has a extensible system for connecting to hardware devices the following robotics and physical computing platforms are currently supported arduino http www arduino cc package https github com hybridgroup gobot tree master platforms firmata audio package https github com hybridgroup gobot tree master platforms audio beaglebone black http beagleboard org boards package https github com hybridgroup gobot tree master platforms beaglebone beaglebone pocketbeagle http beagleboard org pocket package https github com hybridgroup gobot tree master platforms beaglebone bluetooth le https www bluetooth com what is bluetooth technology bluetooth technology basics low energy package https github com hybridgroup gobot tree master platforms ble c h i p http www nextthing co pages chip package https github com hybridgroup gobot tree master platforms chip c h i p pro https docs getchip com chip pro html package https github com hybridgroup gobot tree master platforms chip digispark http digistump com products 1 package https github com hybridgroup gobot tree master platforms digispark dji tello https www ryzerobotics com tello package https github com hybridgroup gobot tree master platforms dji tello dragonboard https developer qualcomm com hardware dragonboard 410c package https github com hybridgroup gobot tree master platforms dragonboard esp8266 http esp8266 net package https github com hybridgroup gobot tree master platforms firmata gopigo 3 https www dexterindustries com gopigo3 package https github com hybridgroup gobot tree master platforms dexter gopigo3 intel curie https www intel com content www us en products boards kits curie html package https github com hybridgroup gobot tree master platforms intel iot curie intel edison http www intel com content www us en do it yourself edison html package https github com hybridgroup gobot tree master platforms intel iot edison intel joule http intel com joule getstarted package https github com hybridgroup gobot tree master platforms intel iot joule jetson nano https developer nvidia com embedded jetson nano package https github com hybridgroup gobot tree master platforms jetson joystick http en wikipedia org wiki joystick package https github com hybridgroup gobot tree master platforms joystick keyboard https en wikipedia org wiki computer keyboard package https github com hybridgroup gobot tree master platforms keyboard leap motion https www leapmotion com package https github com hybridgroup gobot tree master platforms leap mavlink http qgroundcontrol org mavlink start package https github com hybridgroup gobot tree master platforms mavlink megapi http www makeblock com megapi package https github com hybridgroup gobot tree master platforms megapi microbit http microbit org package https github com hybridgroup gobot tree master platforms microbit mqtt http mqtt org package https github com hybridgroup gobot tree master platforms mqtt nanopi neo https wiki friendlyelec com wiki index php nanopi neo package https github com hybridgroup gobot tree master platforms nanopi nats http nats io package https github com hybridgroup gobot tree master platforms nats neurosky http neurosky com products markets eeg biosensors hardware package https github com hybridgroup gobot tree master platforms neurosky opencv http opencv org package https github com hybridgroup gobot tree master platforms opencv particle https www particle io package https github com hybridgroup gobot tree master platforms particle parrot ardrone 2 0 http ardrone2 parrot com package https github com hybridgroup gobot tree master platforms parrot ardrone parrot bebop http www parrot com usa products bebop drone package https github com hybridgroup gobot tree master platforms parrot bebop parrot minidrone https www parrot com us minidrones package https github com hybridgroup gobot tree master platforms parrot minidrone pebble https www getpebble com package https github com hybridgroup gobot tree master platforms pebble radxa rock pi 4 https wiki radxa com rock4 package https github com hybridgroup gobot tree master platforms rockpi raspberry pi http www raspberrypi org package https github com hybridgroup gobot tree master platforms raspi sphero http www sphero com package https github com hybridgroup gobot tree master platforms sphero sphero bb 8 http www sphero com bb8 package https github com hybridgroup gobot tree master platforms sphero bb8 sphero ollie http www sphero com ollie package https github com hybridgroup gobot tree master platforms sphero ollie sphero sprk http www sphero com sprk plus package https github com hybridgroup gobot tree master platforms sphero sprkplus tinker board https www asus com us single board computer tinker board package https github com hybridgroup gobot tree master platforms tinkerboard up2 http www up board org upsquared package https github com hybridgroup gobot tree master platforms upboard up2 support for many devices that use general purpose input output gpio have a shared set of drivers provided using the gobot drivers gpio package gpio https en wikipedia org wiki general purpose input output drivers https github com hybridgroup gobot tree master drivers gpio aip1640 led button buzzer direct pin easydriver grove button grove buzzer grove led grove magnetic switch grove relay grove touch sensor led makey button motor proximity infra red pir motion sensor relay rgb led servo stepper motor tm1638 led controller support for many devices that use analog input output aio have a shared set of drivers provided using the gobot drivers aio package aio https en wikipedia org wiki analog to digital converter drivers https github com hybridgroup gobot tree master drivers aio analog sensor grove light sensor grove piezo vibration sensor grove rotary dial grove sound sensor grove temperature sensor support for devices that use inter integrated circuit i2c have a shared set of drivers provided using the gobot drivers i2c package i2c https en wikipedia org wiki i c2 b2c drivers https github com hybridgroup gobot tree master drivers i2c adafruit 2x16 rgb lcd with 5 keys adafruit motor hat ads1015 analog to digital converter ads1115 analog to digital converter adxl345 digital accelerometer bh1750 digital luminosity lux light sensor blinkm led bme280 barometric pressure temperature altitude humidity sensor bmp180 barometric pressure temperature altitude sensor bmp280 barometric pressure temperature altitude sensor bmp388 barometric pressure temperature altitude sensor drv2605l haptic controller generic driver for read and write values to from register address grove digital accelerometer grovepi expansion board grove rgb lcd hmc6352 compass hmc5883l 3 axis digital compass ina3221 voltage monitor jhd1313m1 lcd display w rgb backlight l3gd20h 3 axis gyroscope lidar lite mcp23017 port expander mma7660 3 axis accelerometer mpl115a2 barometric pressure temperature mpu6050 accelerometer gyroscope pca9501 8 bit i o port with interrupt 2 kbit eeprom pca953x led dimmer for pca9530 2 bit pca9533 4 bit pca9531 8 bit pca9532 16 bit pca9685 16 channel 12 bit pwm servo driver pcf8583 clock and calendar or event counter 240 x 8 bit ram pcf8591 8 bit 4xa d 1xd a converter sht2x temperature humidity sht3x d temperature humidity ssd1306 oled display controller tsl2561 digital luminosity lux light sensor wii nunchuck controller yl 40 brightness temperature sensor potentiometer analog input analog output driver support for devices that use serial peripheral interface spi have a shared set of drivers provided using the gobot drivers spi package spi https en wikipedia org wiki serial peripheral interface bus drivers https github com hybridgroup gobot tree master drivers spi apa102 programmable leds mcp3002 analog digital converter mcp3004 analog digital converter mcp3008 analog digital converter mcp3202 analog digital converter mcp3204 analog digital converter mcp3208 analog digital converter mcp3304 analog digital converter mfrc522 rfid card reader ssd1306 oled display controller more platforms and drivers are coming soon api gobot includes a restful api to query the status of any robot running within a group including the connection and device status and execute device commands to activate the api import the gobot io x gobot v2 api package and instantiate the api like this go master gobot newmaster api newapi master start you can also specify the api host and port and turn on authentication go master gobot newmaster server api newapi master server port 4000 server addhandler api basicauth gort klatuu server start you may access the robeaux https github com hybridgroup robeaux react js interface with gobot by navigating to http localhost 3000 index html cli gobot uses the gort http gort io http gort io command line interface cli so you can access important features right from the command line we call it robotops aka devops for robotics you can scan connect update device firmware and more documentation we re always adding documentation to our web site at https gobot io please check there as we continue to work on gobot thank you need help issues https github com hybridgroup gobot issues twitter gobotio https twitter com gobotio slack https gophers slack com messages c0n5hdb08 https gophers slack com messages c0n5hdb08 mailing list https groups google com forum forum gobotio contributing for our contribution guidelines please go to https github com hybridgroup gobot blob master contributing md https github com hybridgroup gobot blob master contributing md gobot is released with a contributor code of conduct by participating in this project you agree to abide by its terms you can read about it here https github com hybridgroup gobot tree master code of conduct md license copyright c 2013 2020 the hybrid group licensed under the apache 2 0 license the contributor covenant is released under the creative commons attribution 4 0 international public license which requires that attribution be included | robotics iot drone go robot uav arduino raspberry-pi beaglebone beaglebone-black intel-joule sphero bluetooth gpio i2c internet-of-things intel-edison hardware mqtt bluetooth-le | server |
webeng | web engineering web applications are complex systems that deliver a plethora of features to a large number of users including developers and also exhibit unique behaviors and demands in terms of performance scalability usability and security this course will discuss the limits of current web technologies information and service architectures caching session and data management results midterm results 2018 https docs google com spreadsheets d 16vid4 55m8y3a nre15x333n4lnhyum7djk vsock e edit usp sharing project teams progress https drive google com file d 1fkq8 sxktih addndci3h5rudqt4y65n view usp sharing 2018 batches data itm https github com mbilalshaikh webeng 2018 itm ite https github com mbilalshaikh webeng 2018 ite swm https github com mbilalshaikh webeng 2018 swm swe https github com mbilalshaikh webeng 2018 swe software requirement specification srs document https drive google com open id 1kwt obw98xoifujdkjdw33rafh6j116b instructor muhammad bilal shaikh text book web engineering a practitioner s approach https yslaiseblog files wordpress com 2014 03 pressman lowe 2009 web engineering a practitioner s approach pdf lecture slides web engineering a practitioner s approach https drive google com open id 1dwnqn2j2r5qwrmwx2pikeefoakxvhcdr facebook study group http www fb com groups webmobiledevelopers languages to work on html css javascript php mysql softwares xampp prerequisites any basic programming course data structures sylabus plan date agenda deliverable week 1 attributes of web engineering week 2 categories of websites week 3 web development models week 4 water fall model sdlc week 5 spiral model week 6 agile model week 7 design patterns week 8 mvc week 9 mvc example framework i code ignitor week 10 mvc example framework ii laravel week 11 orm object relational model week 12 web frameworks week 13 web services rest week 14 rest model view controller data modeling two really important outcomes for any good computer science curriculum 1 good design and 2 separation of concerns design pattern reusable practice for designing software proven way benefits used by many risks are known avoid reinventing the wheel model view controller mvc design pattern mvc based frameworks ios android rails django backbone js angularjs asp spring let s concentrate on the the model recall the idea of nosql or mongodb and example of a nosql database why nosql database why not a nosql database example name m f title year carrie fisher f star wars a new hope 1977 carrie fisher f the empire strikes back 1980 carrie fisher f return of the jedi 1983 mark hamill m star wars a new hope 1977 mark hamill m the empire strikes back 1980 mark hamill m return of the jedi 1983 harrison ford m star wars a new hope 1977 harrison ford m the empire strikes back 1980 harrison ford m return of the jedi 1983 harrison ford m indiana jones 1981 what is a relational database the relational model tables collection of related records rows records or tuples columns fields adhering to certain data types keys bindings views query results schema blueprint why relational databases mature productive standards exist e g the structured query language extensible complex queries can be done more security options avoids data duplication easier to change data if designed correctly topics presentations persentations in lab 1 2 min 4 max each group projects demo code and final demo lab sylabus plan date agenda deliverable week 1 lab 1 html week 2 lab 2 html extended week 3 lab 3 css startup week 4 lab 4 css extended week 5 lab 5 javasript startup week 6 lab 6 javascript extended week 7 lab 7 mixing html css javascript week 8 lab 8 php startup week 9 lab 9 php fundamentals xampp week 10 lab 10 php i week 11 lab 11 php ii week 12 lab 12 mysql intro week 13 lab 13 mysql extended week 14 lab 14 project practice i week 15 lab 15 project demonstration i week 16 lab 16 project demonstration ii topics presentations persentations in lab 1 2 min 4 max each group projects demo code and final demo course outline improvements for future classes more focus on projects and mysql mongodb based linux vm use to extend course towards nosql databases vm tutorial http www cs tufts edu comp 20 vm sql object relation mapper orm skipped ios android rails django backbone js angularjs asp spring skipped data modeling for regular students theory chapter 1 to 4 from book slides and class notes web engineering attributes categories waterfall model spiral agile software development models web services rest model view controller mvc based frameworks static code optimization scalability lab http www github com mbilalshaikh webeng html css javascript bootstrap php mysql for improvers failures lab http www github com mbilalshaikh webeng html css javascript php theory web engineering attributes categories waterfall model spiral agile software development models web services rest model view controller mvc based frameworks static code optimization scalability | html javascript php css | front_end |
WebDev | website for web developer cloud developer software engineer randy taylor webdev rhtaylor randy this site is hosted at https www rhtaylor development com note the gp pages site was abandoned as react router dom v6 is incompatible with it | cloud |
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guwen-models | p align center br img src assets guwen models png width 500 br p p align center a href https github com ethan yt guwen models issues img alt github issues src https img shields io github issues ethan yt guwen models a a href https github com ethan yt guwen models stargazers img alt github stars src https img shields io github stars ethan yt guwen models a a href https github com ethan yt guwen models blob main license img alt github license src https img shields io github license ethan yt guwen models a p https cclue top guwen models html a href https github com ethan yt cclue img width 49 src https github readme stats vercel app api pin username ethan yt repo cclue bg color 30 e96443 904e95 title color fff text color fff icon color fff show owner true a a href https github com ethan yt guwenbert img width 49 src https github readme stats vercel app api pin username ethan yt repo guwenbert bg color 30 e96443 904e95 title color fff text color fff icon color fff show owner true a guwenbert base hugging face https huggingface co ethanyt guwenbert base guwenbert large hugging face https huggingface co ethanyt guwenbert large guwenbert fs base one drive https 1drv ms u s aubc6k5udq9um1agkbhqwcvcnb7o e xbgssd roberta classical chinese base char hugging face https huggingface co koichiyasuoka roberta classical chinese base char guwenbert roberta classical chinese large char hugging face https huggingface co koichiyasuoka roberta classical chinese large char sikubert hugging face https huggingface co siku bert sikubert sikuroberta hugging face https huggingface co siku bert sikuroberta guwen x cclue https github com ethan yt cclue guwen seg https huggingface co ethanyt guwen seg guwenbert fs base p align center a href https huggingface co ethanyt guwen seg img width 500 src assets guwen seg png a p guwen punc https huggingface co ethanyt guwen punc guwenbert fs base p align center a href https huggingface co ethanyt guwen punc img width 500 src assets guwen punc png a p guwen quote https huggingface co ethanyt guwen quote guwenbert fs base transformers crf crf example ipynb crf example ipynb p align center a href https huggingface co ethanyt guwen quote img width 500 src assets guwen quote png a p guwen ner https huggingface co ethanyt guwen ner guwenbert base crf crf example ipynb crf example ipynb p align center a href https huggingface co ethanyt guwen ner img width 500 src assets guwen ner png a p guwen cls https huggingface co ethanyt guwen cls guwenbert fs base p align center a href https huggingface co ethanyt guwen cls img width 500 src assets guwen cls png a p guwen sent https huggingface co ethanyt guwen sent guwenbert base p align center a href https huggingface co ethanyt guwen sent img width 500 src assets guwen sent png a p opencc https github com byvoid opencc zhconv https github com gumblex zhconv zh hans jiayan https github com jiaeyan jiayan nlp ud kanbun https github com koichiyasuoka ud kanbun daizhigev20 https github com garychowcmu daizhigev20 v2 0 chinese poetry https github com chinese poetry chinese poetry classical chinese https github com bangboom classical chinese nlp issue ethanyt at qq com | ai |
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android-mini-projects | android mini projects get started with mobile development by building projects choose a project from the list below or suggest your own project and checkout contribution md contribution md to know all the contribution details project list bmi calculator portfolio website 3d website snake game tic tac toe | hacktoberfest | front_end |
machine-learning-programming-assignments-coursera-andrew-ng | machine learning programming assignments coursera andrew ng solutions to andrew ng s machine learning course on coursera | ai |
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jekyllcodex | jekyll codex a website for front end developers who want to learn jekyll https jekyllcodex org jekyll codex is created by joost van der schee jhvanderschee https twitter com jhvanderschee or joost vdschee nl to make it easier for fellow front end developers to choose static web technology in their next project the getting started guide even shows how to do that without touching the command line you can also watch jekyllcodex at jekyllconf 2019 https vimeo com 361839295 which shows exactly that in under twenty minutes note that this video uses the jquery and twitter bootstrap version https fresh butterfly cloudvent net of this website if you want to know more about jekyll please visit the official jekyll documentation the link can be found in the menu at the top why jekyll jekyll is the most popular static site generator static sites are better for performance they re cheaper to build and maintain and they re much more secure than traditional database driven websites thus they offer a great added value to front end developers and their clients copyrights feel free to use all code presented on this website under the mit license see below the code of the website itself may also be used as this website is a demo of the code it presents however respect the copyright of the used trademarks and the licenses of the open source software used as their rights belong to their respective owners all images are cc0 images from unsplash mit license copyright c 2020 usecue bv permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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jigsaw | jigsaw for the desktop rdkmaster jigsaw nbsp nbsp nbsp nbsp nbsp npm version https badge fury io js 40rdkmaster 2fjigsaw svg https www npmjs com package rdkmaster jigsaw jigsaw https circleci com gh rdkmaster jigsaw svg style svg https app circleci com pipelines github rdkmaster for mobile rdkmaster jigsaw mobile npm version https badge fury io js 40rdkmaster 2fjigsaw mobile svg https www npmjs com package rdkmaster jigsaw mobile build status https travis ci org rdkmaster jigsaw svg branch v1 1 https travis ci org rdkmaster jigsaw font icons rdkmaster icon font nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp npm version https badge fury io js 40rdkmaster 2ficon font svg https www npmjs com package rdkmaster icon font amount https badgen net badge amount 2431 green https jigsaw zte gitee io latest assets icon font iconfont html online ide edit jigsaw in one click nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp gitpod ready to code https img shields io badge gitpod ready to code blue logo gitpod https gitpod io https github com rdkmaster jigsaw readme readme zh md why jigsaw jigsaw is a complete and powerful web components set the current version contains 56 components 7 containers 7 services and 9 directives basically covering all aspects of web applications simply put jigsaw has almost all the functions of other component sets and jigsaw is more powerful and has better performance therefore there is no need to worry about the lack of functionality with jigsaw jigsaw has an ability that other component sets do not have jigsaw s application can support multiple ux specifications at the same time in one development click here https jigsaw zte gitee io latest components guide supports multiple ux specifications to learn more about it list of availables components name description link 1 alert a simple dialog that usually used to tell the users something important and it can also collect choice of the users demo https jigsaw zte gitee io latest components alert demo popup 2 alphabetical index a list that can be indexed alphabetically demo https jigsaw zte gitee io latest components alphabetical index demo basic 3 alphabetical index select a drop down list that can be indexed alphabetically demo https jigsaw zte gitee io latest components alphabetical index demo select mode 4 auto input a full featured input https jigsaw zte gitee io latest components input demo full component with a drop down list of suggested optional values demo https jigsaw zte gitee io latest components auto complete input demo basic 5 breadcrumb a lightweight navigator which can automaticly trace while the users are browsing demo https jigsaw zte gitee io latest components breadcrumb demo router 6 button a button with multiple states demo https jigsaw zte gitee io latest components button demo full 7 button bar a button bar which supports selection status single or multiple selection it can be used as a form control or a navigator demo https jigsaw zte gitee io latest components button bar demo basic 8 cascade a form control for collecting or presenting data with subordinate relationships demo https jigsaw zte gitee io latest components cascade demo search and paging 9 charticon a componnet for rendering a small area into charts supporting pie line and bar charts demo https jigsaw zte gitee io latest components chart icon demo basic 10 checkbox a form control for collecting multi choices demo https jigsaw zte gitee io latest components checkbox demo full 11 color select a form control that provides multiple ways to pick colors demo https jigsaw zte gitee io latest components color select demo basic 12 date picker a form control used to collect dates demo https jigsaw zte gitee io latest components date picker demo basic 13 date time single a form control for collecting date and time demo https jigsaw zte gitee io latest components date time picker demo basic 14 date time select a form control used to collect date and time it provides choices in a drop down manner demo https jigsaw zte gitee io latest components date time picker demo date time select 15 date time range a form control used to collect the start and end value including date and time demo https jigsaw zte gitee io latest components range date time picker demo basic 16 date time select a form control used to collect the start and end value including date and time it provides choices in a drop down manner demo https jigsaw zte gitee io latest components range date time picker demo range date time select 17 fish bone a fishbone graph are often used to present some data with subordinate relationships in the form of fish bones demo https jigsaw zte gitee io latest components fish bone demo full 18 graph render any data graphically including any graphics such as bar charts line charts pie charts gauge and more powered by echarts echarts baidu com demo https jigsaw zte gitee io latest components graph demo pie 19 header a simple component used to prompt the topic of a certain area on the ui demo https jigsaw zte gitee io latest components header demo basic 20 icon a font icon which can be used as a link button supports font aweasome and jigsaw s internal icon libraries https www npmjs com package rdkmaster icon font demo https jigsaw zte gitee io latest components icon demo basic 21 input normal a form control used to collect a single line of text demo https jigsaw zte gitee io latest components input demo full 22 input search a search box for fuzzy matching according to text demo https jigsaw zte gitee io latest components search input demo basic 23 list lite a control for presenting a group of data in a text list supports single selection and multiple selection supports main title and subtitle and supports icons demo https jigsaw zte gitee io latest components list lite demo full 24 list an enhanced version of the list lite https jigsaw zte gitee io latest components list lite demo full control that supports the presentation of a set of data in any form demo https jigsaw zte gitee io latest components list demo full 25 loading displays an animation to ease the user s anxiety during waiting for certain operations demo https jigsaw zte gitee io latest components loading demo full 26 menu a multi level menu control which is often used for navigation demo https jigsaw zte gitee io latest components menu demo navigation 27 notification informs some unimportant message in the corner of the ui or to collect some unimportant choice of the users demo https jigsaw zte gitee io latest components notification demo full 28 numeric input a form control for collecting numbers without typing supports floats and integers demo https jigsaw zte gitee io latest components numeric input demo step 29 pagination a control used to divide a large amount of data into multiple pages for display not only can cooperate with the table component but also can realize the paging operation of any data demo https jigsaw zte gitee io latest components pagination demo basic 30 progress bar a horizontal progress bar demo https jigsaw zte gitee io latest components progress demo full 31 progress circle a circular progress bar demo https jigsaw zte gitee io latest components progress demo circle progress 32 progress status a process status component which can be used to display various states in the process demo https jigsaw zte gitee io latest components process status demo basic 33 radio a form control for collecting single choices demo https jigsaw zte gitee io latest components radio group demo full 34 rate a form control for the users to give his her score of something demo https jigsaw zte gitee io latest components rate demo full 35 scrollbar a custom scrollbar to any container powered by perfect scrollbar https github com utatti perfect scrollbar demo https jigsaw zte gitee io latest components scrollbar demo basic 36 select normal a form control for collecting the choice s by dropping down a list demo https jigsaw zte gitee io latest components select demo basic 37 select group a form control used to collect options through a drop down list supports option grouping and is often used in situations where options are more complex demo https jigsaw zte gitee io latest components select group demo select group 38 select collapse a form control used to collect options through a drop down list it supports folding to group options it is often used in situations where options are more complicated demo https jigsaw zte gitee io latest components select collapse demo select collapse 39 signaling chart a signaling flow chart demo https jigsaw zte gitee io latest components table demo swim lane diagram 40 slider a form control for collecting numerical info by sliding demo https jigsaw zte gitee io latest components slider demo basic 41 steps displays a series of custom steps which have some predefined states demo https jigsaw zte gitee io latest components steps demo basic 42 switch a form control for collection yes no or on off choices demo https jigsaw zte gitee io latest components switch demo size 43 tab bar a tab switcher for the tabs container https jigsaw zte gitee io latest components tab demo api demo https jigsaw zte gitee io latest components tab bar demo type 44 table a very very powerful data grid demo https jigsaw zte gitee io latest components table demo renderer 45 tag a tag control demo https jigsaw zte gitee io latest components tag demo basic 46 textarea a form control for collecting multi lines of text demo https jigsaw zte gitee io latest components textarea demo basic 47 tile lite a list that displays data by horizontal tiling and the list https jigsaw zte gitee io latest components list lite demo full component tiling data vertically demo https jigsaw zte gitee io latest components tile lite demo full 48 tile an enhanced version of the tile https jigsaw zte gitee io latest components tile lite demo full control demo 49 time a time selector for selecting hours minutes and seconds demo https jigsaw zte gitee io latest components time picker demo basic 50 time section a time rule selector you can set the rule for matching time demo https jigsaw zte gitee io latest components time section demo basic 51 toast a component used to prompt timely messages without intrusion demo https jigsaw zte gitee io latest components toast demo full 52 transfer a complex selector used to select a large number of entries supports paging demo https jigsaw zte gitee io latest components transfer demo basic 53 tree renders some data with subordinate relationships as a tree powered by ztree http www treejs cn v3 main php ztreeinfo demo https jigsaw zte gitee io latest components tree demo icon 54 upload opens a file explorer to select one or more files and upload to the sever demo https jigsaw zte gitee io latest components upload demo basic 55 upload result opens a file explorer to select one or more files and upload to the sever demo https jigsaw zte gitee io latest components upload demo upload result 56 viewport represents part of an abstract view demo https jigsaw zte gitee io latest components table demo big table containers name description link 1 box a powerful view layout based on flex which abstracts the view into horizontal and vertical boxes and uses their mutual combination to quickly make the layout of complex views demo https jigsaw zte gitee io latest components box demo middle resize line 2 editable box a data driven box layout that makes it easier to achieve dynamic layout editing effects at runtime demo https jigsaw zte gitee io latest components box demo layout interaction 3 collapse a container which can fold or open the given view demo https jigsaw zte gitee io latest components collapse demo full 4 combo select a container that can hide any given view which the user can pull down to display this view demo https jigsaw zte gitee io latest components combo select demo searchable 5 dialog a dialog box component which is often used in conjunction with popupservice https jigsaw zte gitee io latest components api injectable popupservice demo https jigsaw zte gitee io latest components dialog demo popup option 6 drawer a drawer which is often used to show hide complex views demo https jigsaw zte gitee io latest components drawer demo drawer in drawer 7 tabs a multi view folding container with tabs which can overlay multiple views together demo https jigsaw zte gitee io latest components tab demo api services name description link 1 theme system jigsaw provides a very powerful theme system which can support a variety of ux specification theme support online hot switching of dark and light skins support the use of opposite color theme in local areas and also provide a set of css variables are used to help users create pages that support each of the above features demo https jigsaw zte gitee io latest components theme demo properties 2 data jigsaw encapsulates many kinds of data objects to help applications more easily to feed data to all the controls demo https jigsaw zte gitee io latest components data encapsulation demo array ssp 3 loadingservice popups up and manages loading https jigsaw zte gitee io latest components loading demo full component demo https jigsaw zte gitee io latest components loading demo full 4 popupservice popups any given view to the top of the ui very powerful demo https jigsaw zte gitee io latest components dialog demo popup option 5 timeservice translate time macros like now 1d to real values 6 translation used to create a view that supports multiple languages demo https jigsaw zte gitee io latest components i18n demo full 7 charticon render simple data to tiny charts directives name description link 1 badge add a badge to any view to grab the user s attention and support multiple forms of badge demo https jigsaw zte gitee io latest components badge demo basic 2 menu add multi level menu function to any view or popup a context menu demo https jigsaw zte gitee io latest components menu demo options 3 download graphs add a function of downloading screenshots of all the graphics in the host container demo https jigsaw zte gitee io latest components graph demo download directive 4 drag and drop makes the host draggable and droppable demo https jigsaw zte gitee io latest components drag drop demo 5 float drop down any given view near the host many positions supported demo https jigsaw zte gitee io latest components float demo pos reviser 6 movable the ability to add to any view so that the view can be dragged by the mouse and follow the movement of the mouse demo https jigsaw zte gitee io latest components badge demo move 7 tooltip add a context prompt to any view support rich text and support interaction demo https jigsaw zte gitee io latest components tooltip demo html renderer 8 trusted html similar to angular s innerhtml directive without sanitizing the given trusted html support interaction demo https jigsaw zte gitee io latest components trusted html demo full 9 upload adding the file upload function to any view needs to be used in conjunction with the upload result https jigsaw zte gitee io latest components upload demo upload result control demo https jigsaw zte gitee io latest components upload demo upload result meaning of name we name this suite of components from a puzzle game the process of this game during which the player combine the messy pieces into a picture in accordance with the established blueprint is similar to the development process of modern web page we use jigsaw as this component set s name hoping to make web page developers to combine the messy pieces of functions into your web page just like playing jigsaw puzzle the soul of jigsaw is combination and we are committed to develop combination to the extreme level when several components are sorted and arranged in a certain order you can get an application page this is a normal combination which we definee as level i combination in this level all the components are like atoms which means they can only act what they were made but jigsaw s components are no longer atoms we made a secondary abstraction of the functionality of the components while allowing parts of the components highly customizable some component even fully customizable small to basic components like jigsaw button large to giant components such as jigsaw table almost every ui element you see can be combined with other components to change its default behavior and therefor to enhance the capability of components atomic components are limited but the combination can produce infinite possibilities the customization mentioned here in other words is another form of combination and we call this level of combination as level ii with jigsaw unleash your imagination get started a brand new start with jigsaw we strongly recommend to use jigsaw seed https github com rdkmaster jigsaw seed as the seed of all new projects the specific steps are 1 install or update nodejs https nodejs org node 6 x x and npm 3 x x or later is required 2 download https github com rdkmaster jigsaw seed archive master zip or clone https github com rdkmaster jigsaw seed the source of jigsaw seed assumed being saved to d jigsaw seed 3 install all dependencies by excuting the following script cd d jigsaw seed npm config set registry https registry npm taobao org for chinese developers only npm config set sass binary site https npm taobao org mirrors node sass for chinese developers only npm install g angular cli optional but strongly recommended npm install npm start attensions to the chinese developers do not use cnpm to install the dependencies because of this issue https github com cnpm cnpmjs org issues 1463 attensions to all zters you can use the npm mirror inside of zte for faster speed of installing check this link for more details docs how to use npm mirror inside of zte index md 4 open http localhost 4200 in your browser your development environment is completed set up if you can see the welcome page 5 afterwards you can directly run npm start in the d jigsaw seed directory to start the development environment 6 jigsaw has specifically done code optimization for the modern ide so that ide can accurately prompt more information saving your time of reading api document we recommend using webstorm https www jetbrains com webstorm as ide use jigsaw with your developing project see the specific process here docs integrate your project with jigsaw index md a bible for the beginners jigsaw tourist https github com rdkmaster jigsaw tourist is a tutorial dedication to the beginners which shows how to use jigsaw from scratch to build a simple application page click here docs tourist index md bravely take your first step in jigsaw if there is any difficulty in getting started please add jigsaw s official wechat where you can join the sos group and have a dialogue with our developers directly docs image qr weixin jpg a advanced guide any badge https github com rdkmaster any badge is a best practice of using jigsaw and rdk https github com rdkmaster rdk to create a full featured web application it is a very good choice to read the source code of any badge https github com rdkmaster any badge which helps your to learn more about jigsaw and rdk after you finish reading the tour of heroes https angular io tutorial and the jigsaw tourist https github com rdkmaster jigsaw blob master docs tourist index md one more star please this is the best encouragement for us contribution we believe that the following acts are doing contributions quietly concern about jigsaw watch star fork it reporting a bug or give us any suggestions by submitting an issue https github com rdkmaster jigsaw issues new write or translate the api documentation or any articals about jigsaw the more effective way to contribute is to push us prs all prs are welcome and will be dealt with seriously give priority to the issues https github com rdkmaster jigsaw issues without a suspend tag this is a simple code specification docs coding spec md please try to follow it | webui component bigdata typescript jigsaw jigsaw-seed angular zte | front_end |
esp8266-ir-remote | esp8266 ir remote infrared remote control library for esp8266 sdk the library itself consist of next files include ir remote h include ir remote def h user ir remote c it use frc1 timer interrupt to create precise pwm signal with specified frequency the library is capable to send nec panasonic sony samsung rc5 rc6 and raw codes it s not intended to decode signals ir code defines are based on https github com shirriff arduino irremote by ken shirriff makefile used from cherts esp8266 dev kit https github com cherts esp8266 devkit the library is distributed as an example application that can be flashed to esp8266 modify makefile according to your esp8266 sdk environment change serial port run make run make flash currently tested only nec protocol but others should work too | os |
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pml-book | probabilistic machine learning a book series by kevin murphy p nbsp p book 0 machine learning a probabilistic perspective 2012 see this link https probml github io pml book book0 html see this link https probml github io pml book pml0 book0 html book 1 probabilistic machine learning an introduction 2022 see this link https probml github io pml book book1 html book 2 probabilistic machine learning advanced topics 2023 see this link https probml github io pml book book2 html | ai |
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cvclasses18 | miet https avatars0 githubusercontent com u 20048671 s 80 computer vision classes 2018 build status https travis ci com cvlabmiet cvclasses18 svg token 1uulw4ub9cpzqjjxqhst branch master https travis ci com cvlabmiet cvclasses18 overview this is a computer vision classes prepared for the 2nd year m d students course includes topics covering basic areas of computer vision it takes 16 seminars discussion lessons and 8 practical lessons most practical lessons will be based on using of the opencv v3 2 library all materials and tasks will be hosted in this repository and available for all students all program code is written using c 14 required knowledge basics of digital signal processing basics of digital image processing git experience c experience course plan course consist of 26 seminars practical lessons 7 individual programming tasks 2018 plan https docs google com spreadsheets d e 2pacx 1vqed4iwdoidpr h6e k1bkvmh xq2l4fgpdfija8epcvm5a7nwc1s1empoc5fbutakbmvkuwzslpic8 pubhtml gid 1805943559 single true repository overview the structure of repository is described below cmake contains cmake helper functions for checkstyle and ctest cvlib simple computer vision library to be developed in this course include public api src internal sources tests unit tests based on catch2 framework demo demo application based on algorithms of cvlib tools overview please ensure you have following instruments and settings to start the work git https git scm com cmake 3 9 https cmake org opencv 3 2 http opencv org downloads html gcc 7 3 visual studio 14 2015 win64 camera to execute demo applications build steps 1 register github account if you haven t it yet 2 meet with git https git scm com book ru v1 and practice locally 3 fork current repository https github com cvlabmiet cvclasses18 4 configure git locally https git scm com book en v2 getting started first time git setup linux prerequesties sh sudo apt install gcc 7 sudo apt install libopencv dev build stay in root folder of the repository sh mkdir build cd build cmake make checkstyle make ctest run run unit tests sh build cvlib cvlib tests run demo sh build demo cvlib demo windows prerequesties 1 install latest cmake 3 9 2 x64 2 download and unpack binaries of opencv 3 2 vc14 3 add system environment variables example opencv dir c users roman downloads opencv build path opencv dir build x64 vc14 bin 4 install visual studio 2015 or 2017 if you use visual studio 2017 then build tools 2015 shall be installed build stay in root folder of the repository sh mkdir build cd build build cmake g visual studio 14 2015 win64 build cmake build config release note checkstyle is disabled in win32 environment run demo sh build demo release cvlib demo exe contacts any issues suggestions questions may be asked directly by email golovanov org miet ru created as separate issue in this repository https github com cvlabmiet cvclasses18 issues | ai |
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Online-Grosery-Shop-Project | online grosery shop project the digital market increased in various sectors from clothes shopping to grocery shopping only the grocery business has seen enormous development in the last 3 4 years there are several start ups smes and companies that are investing in the grocery business online mainly they are developing characteristic rich grocery mobile applications you can have this doubt about how you can keep up with online grocery stores the recent covid 19 pandemic has played as motivation in increasing demand for these services retail healthcare production or management in every sector wants to increase the online shopping experience by providing better customer services some of the leading grocery apps available in the global market are amazon fresh peapod big basket etc these services are becoming more and more popular as it is saving them time and efforts of the people by providing them with a comfortable facility software requirements front end net beans java development back end xampp server mysql server apache server front end and backend connectivity mysql jdbc driver | server |
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ggml | ggml roadmap https github com users ggerganov projects 7 manifesto https github com ggerganov llama cpp discussions 205 tensor library for machine learning note that this project is under active development some of the development is currently happening in the llama cpp https github com ggerganov llama cpp and whisper cpp https github com ggerganov whisper cpp repos features written in c 16 bit float support integer quantization support 4 bit 5 bit 8 bit etc automatic differentiation adam and l bfgs optimizers optimized for apple silicon on x86 architectures utilizes avx avx2 intrinsics on ppc64 architectures utilizes vsx intrinsics no third party dependencies zero memory allocations during runtime updates x example of gpt 2 inference examples gpt 2 https github com ggerganov ggml tree master examples gpt 2 x example of gpt j inference examples gpt j https github com ggerganov ggml tree master examples gpt j x example of whisper inference examples whisper https github com ggerganov ggml tree master examples whisper x support 4 bit integer quantization https github com ggerganov ggml pull 27 x example of cerebras gpt inference examples gpt 2 https github com ggerganov ggml tree master examples gpt 2 example of flan t5 inference https github com ggerganov ggml pull 12 x example of llama inference ggerganov llama cpp https github com ggerganov llama cpp x example of llama training ggerganov llama cpp examples baby llama https github com ggerganov llama cpp tree master examples baby llama x example of falcon inference cmp nct ggllm cpp https github com cmp nct ggllm cpp x example of bloom inference nouamanetazi bloomz cpp https github com nouamanetazi bloomz cpp x example of rwkv inference saharnooby rwkv cpp https github com saharnooby rwkv cpp x example of sam inference examples sam https github com ggerganov ggml tree master examples sam x idea for gpu support https github com ggerganov llama cpp discussions 915 x example of stablelm gpt neox inference examples gpt neox https github com ggerganov ggml tree master examples gpt neox x example of bert inference skeskinen bert cpp https github com skeskinen bert cpp x example of starcoder inference examples starcoder https github com ggerganov ggml tree master examples starcoder x example of mpt inference examples mpt https github com ggerganov ggml tree master examples mpt x example of replit inference examples replit https github com ggerganov ggml tree master examples replit x example of biogpt inference pabannier biogpt cpp https github com pabannier biogpt cpp x example of encodec inference pabannier encodec cpp https github com pabannier encodec cpp x example of clip inference monatis clip cpp https github com monatis clip cpp x example of minigpt4 inference maknee minigpt4 cpp https github com maknee minigpt4 cpp x example of chatglm inference li plus chatglm cpp https github com li plus chatglm cpp x example of stable diffusion inference leejet stable diffusion cpp https github com leejet stable diffusion cpp x example of qwen inference qwenlm qwen cpp https github com qwenlm qwen cpp whisper inference example with ggml you can efficiently run whisper examples whisper inference on the cpu memory requirements model disk mem tiny 75 mb 280 mb base 142 mb 430 mb small 466 mb 1 0 gb medium 1 5 gb 2 6 gb large 2 9 gb 4 7 gb gpt inference example with ggml you can efficiently run gpt 2 examples gpt 2 and gpt j examples gpt j inference on the cpu here is how to run the example programs bash build ggml examples git clone https github com ggerganov ggml cd ggml mkdir build cd build cmake make j4 gpt 2 gpt j run the gpt 2 small 117m model examples gpt 2 download ggml model sh 117m bin gpt 2 m models gpt 2 117m ggml model bin p this is an example run the gpt j 6b model requires 12gb disk space and 16gb cpu ram examples gpt j download ggml model sh 6b bin gpt j m models gpt j 6b ggml model bin p this is an example install python dependencies python3 m pip install r requirements txt run the cerebras gpt 111m model download from https huggingface co cerebras python3 examples gpt 2 convert cerebras to ggml py path to cerebras gpt 111m bin gpt 2 m path to cerebras gpt 111m ggml model f16 bin p this is an example the inference speeds that i get for the different models on my 32gb macbook m1 pro are as follows model size time token gpt 2 117m 5 ms gpt 2 345m 12 ms gpt 2 774m 23 ms gpt 2 1558m 42 ms gpt j 6b 125 ms for more information checkout the corresponding programs in the examples examples folder using metal only with gpt 2 for gpt 2 models offloading to gpu is possible note that it will not improve inference performances but will reduce power consumption and free up the cpu for other tasks to enable gpu offloading on macos bash cmake dggml metal on dbuild shared libs off add ngl 1 bin gpt 2 t 4 ngl 100 m models gpt 2 117m ggml model bin p this is an example using cublas bash fix the path to point to your cuda compiler cmake dggml cublas on dcmake cuda compiler usr local cuda 12 1 bin nvcc using clblast bash cmake dggml clblast on resources ggml large language models for everyone https github com rustformers llm blob main crates ggml readme md a description of the ggml format provided by the maintainers of the llm rust crate which provides rust bindings for ggml marella ctransformers https github com marella ctransformers python bindings for ggml models go skynet go ggml transformers cpp https github com go skynet go ggml transformers cpp golang bindings for ggml models smspillaz ggml gobject https github com smspillaz ggml gobject gobject introspectable wrapper for use of ggml on the gnome platform | automatic-differentiation large-language-models machine-learning tensor-algebra | ai |
blockchain_learning | blockchain this repo records my blockchain source code reading if you got problems join this telegram group for help https t me jj rok3eof40nzm1 | blockchain |
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anlp | anlp contains demo codes for the course applied natural language processing | natural-language-processing demos neural-network deep-learning-algorithms machine-translation | ai |
ros2_llm | ros2 llm framework for controlling ros2 robots with large language models | ai |
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javascript-and-jquery-by-jon-duckett | javascript and jquery by jon duckett code samples from javascript amp jquery interactive web development by jon duckett | front_end |
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mscs_image_content | starter code for br microsoft cognitive services image content analysis introduction currently there is no official r package for calling microsoft cognitive services apis from r the microsoft cognitive services api is a standard rest api which can be called by using the httr package input and output are standard json which we can create and extract using the jsonlite package image content analysis feature is powered by the computer vision api it returns information about visual content found in an image the algorithms use tagging descriptions and domain specific models to identify content and label it with confidence here are some cool applications developed in r which are run by the powerful microsoft cognitive services algorithms applications 1 image content analysis r shiny application https jayendrashinde91 shinyapps io mscs image content mscs image content snapshot images mscs image content png 2 twitterbot powered by microsoft computer vision algorithms telltalebot http telltalebot herokuapp com telltalebot in action images telltalebot action png | ai |
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Machine-Learning-Scientist-with-Python-by-DataCamp | machine learning scientist with python by datacamp python programming skill set with the toolbox to perform supervised unsupervised and deep learning learn how to process data for features train your models assess performance and tune parameters for better performance natural language processing image processing and popular libraries such as spark and keras my datacamp profile and certificates datacamp profile https www datacamp com profile s1551310621 certificate link machine learning path https www datacamp com statement of accomplishment track e31c6c92a2f9cb8458148845b0ac0b9e37494168 | ai |
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aiexperiments-drum-machine | the infinite drum machine thousands of everyday sounds organized using machine learning about sounds are complex and vary widely this experiment uses machine learning to organize thousands of everyday sounds the computer wasn t given any descriptions or tags only the audio using a technique called t sne the computer placed similar sounds closer together you can use the map to explore neighborhoods of similar sounds and even make beats using the drum sequencer https aiexperiments withgoogle com drum machine https aiexperiments withgoogle com drum machine this is not an official google product credits built by kyle mcdonald https github com kylemcdonald manny tan https github com mannytan yotam mann https github com tambien and friends at google creative lab https github com googlecreativelab thanks to the philharmonia orchestra london http www philharmonia co uk for contributing some sounds to this project check out more at a i experiments https aiexperiments withgoogle com overview this application is the visualizer and drum machine from this site https aiexperiments withgoogle com drum machine in this repo you will find all of the front end code which visualizes plays back and makes beats with the audio samples though it does not contain any audio files or the t sne generated from those audio files you can do that with your own samples by following some of the resources below installation to build the client side javascript first install node https nodejs org and webpack https webpack github io then you can install of the dependencies and build the files by typing the following in the terminal bash npm install webpack p creating t sne map if you have a large audio dataset you can convert your sounds to t sne map using some of the python scripts found in kyle mcdonald s audionotebooks https github com kylemcdonald audionotebooks repo our t sne was made by running the following scripts first we collect all of our audio files into a single numpy array using collect samples ipynb and created one large wave file from all of the audio samples to audio spritesheet ipynb then we generated audio fingerprints and turn the fingerprints into a t sne map by running samples to fingerprints ipynb and then fingerprint to t sne ipynb the t sne data is put into two tsv files one has all of the 2d coordinates and the second has is the t sne but projected into 3d space instead of 2d in our experiment use the 2d coordinates for the position on the map and the 3d coordinates for the color of the data point in the scripts folder is some more scripts that we used for processing the audio and t sne data for our application to make these sounds work better as a drum machine we ran an analysis on all of the sounds comparing them to a traditional kick snare open and closed hihat using librosa https github com librosa librosa you can see this script in scripts analysis py this generates a tsv with values for the distance of each of our audio samples from these drum reference sounds using scripts sort sounds py you can create a json structure the most similar sounds to these reference sounds this array is used for picking some random defaults when users first open the experiment and the analysis tsv is used for sorting the rest of the sounds into the 4 drum parts when a user clicks the random button with a filter in the search box to deploy our audio files and data in our experiment we ran two additional step first we separated our very large single wave file into many small mp3s to make loading faster and so that it was playable even if all of the sounds weren t loaded yet and second we created matching json meta files for each of the audio chunks which included the position data color data analysis data and meta data this is specific to our audio set which also included a lot of meta data tags license copyright 2016 google inc all rights reserved licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | ai |
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car-detection | car detection project dependencies note this project was developed on os x yosemite v10 10 5 the web app was also tested and deployed on an ubuntu 15 10 x64 server changes will likely be required to make the code work on other operating systems for several modules numpy opencv 3 1 0 build with extra contributed modules pyaml pillow progress matplotlib skimage scikit image for sliding window generation for trainhog mongodb https docs mongodb org manual administration install community pymongo useful for correcting image orientations see cardetection detection cascadetraining checkimageorientation exifread for imagenet downloader beautifulsoup4 for carpark rendering cyglfw3 pyopengl to build the web app install node and npm install bower npm install g bower install dependencies run in the root directory pip install r requirements txt run in webapp cardetector npm install bower install note the app structure was based on https realpython com blog python the ultimate flask front end deploy web app to a digitalocean ubuntu 15 10 x64 server note the droplet tested had the following specs 20 mo 2gb ram 2 cpus 40gb ssd disk 3 tb transfer new york 2 ubuntu 15 10 x64 add this script to user data when creating the droplet https github com digitalocean do user scripts blob master ubuntu 14 04 web servers lamp yml note see the following for a guide on the nodejs installation below https www digitalocean com community tutorials how to install node js on an ubuntu 14 04 server install the following sudo apt get update sudo apt get install build essential sudo apt get install python dev sudo apt get install python pip sudo apt get install pkg config sudo apt get install libopenblas dev sudo apt get install gfortran sudo apt get install libfreetype6 dev sudo apt get install libpng12 dev sudo apt get install libjpeg turbo8 dev sudo apt get install python numpy sudo apt get install python scipy sudo apt get install libglfw3 dev sudo apt get install python opencv sudo apt get install git sudo apt get install nodejs npm sudo wget qo https raw githubusercontent com creationix nvm v0 31 0 install sh bash install nvm source bashrc nvm install 5 1 0 install a recent version of nodejs nvm use 5 1 0 nvm alias default 5 1 0 ensure that this version is automatically selected when a new session spawns sudo apt get install libapache2 mod wsgi cd var www git clone https github com mmetcalfe car detection cd var www car detection pip install r requirements txt npm install g bower npm install g gulp install dependencies and build the app cd var www car detection webapp cardetector npm install bower install allow root gulp just kill with ctrl c once it s done increase the swap space of the droplet to allow tensorflow to build without g crashing note this happens even with bazel build jobs 1 https www digitalocean com community tutorials how to add swap on ubuntu 14 04 sudo fallocate l 8g swapfile sudo chmod 600 swapfile sudo mkswap swapfile sudo swapon swapfile sudo echo swapfile none swap sw 0 0 etc fstab install tensorflow from source note see the following https www tensorflow org versions r0 7 get started os setup html install bazel sudo apt get install software properties common sudo apt get install openjdk 8 jdk sudo apt get install pkg config zip g zlib1g dev unzip wget https github com bazelbuild bazel releases download 0 1 5 bazel 0 1 5 installer linux x86 64 sh chmod x bazel 0 1 5 installer linux x86 64 sh bazel 0 1 5 installer linux x86 64 sh install other dependencies sudo apt get install python numpy swig python dev clone the tensorflow repository cd git clone recurse submodules https github com tensorflow tensorflow configure the installation accept defaults cd tensorflow configure create the pip package and install note set jobs to a low number to avoid g running out of memory and crashing http stackoverflow com a 34399184 3622526 bazel build jobs 1 c opt tensorflow tools pip package build pip package bazel bin tensorflow tools pip package build pip package tmp tensorflow pkg sudo pip install u tmp tensorflow pkg tensorflow 0 7 0 py2 none any whl whl name may be different check that the app starts without error use ctrl c to quit cd var www car detection webapp cardetector npm test copy the virtual host config and enable the virtual host note see the following for more information https www digitalocean com community tutorials how to deploy a flask application on an ubuntu vps see also http flask pocoo org docs 0 10 deploying mod wsgi creating a wsgi file sudo a2enmod wsgi enable wsgi cd var www car detection webapp cardetector sudo cp cardetector conf etc apache2 sites available a2ensite cardetector enable the virtual host service apache2 restart restart apache to apply changes note make sure to change the servername in cardetector conf to the ip address of your droplet upload image data to the website assume we have a local directory images containing images tar cf imgdata tar images scp imgdata tar root 162 243 238 167 then on the server mkdir var www car detection data images mv imgdata tar var www car detection data images cd var www car detection data images tar xf imgdata tar consider changing permissions chmod r 644 images mv images move contents of archive into current directory create the cache directory for caching detection images cd var www car detection webapp cardetector mkdir cardetector static cache chown www data cardetector static cache upload tensorflow checkpoint data to the website assume we have a local directory cnn train containing the tensorflow checkpoint tar czf cnn train tar cnn train scp cnn train tar root 162 243 238 167 then on the server mkdir var www car detection output mv cnn train tar var www car detection output cd var www car detection output tar xzf cnn train tar create the cache directory for caching detection images cd var www car detection webapp cardetector mkdir cardetector static cache chown www data cardetector static cache to train a cascade classifier python m cardetection detection perform experiments trains and evaluates a set of classifiers based on the supplied template yaml file note a directory containing bounding box data files specifying the positive training samples must be present this can be created using the annotation tool the imagenet downloader or by converting the labels from the kitti dataset to train a hog linear svm classifier start the mongodb driver mongod config usr local etc mongod conf in another terminal run python m cardetection detection trainhog trains a single classifier based on the supplied classifier yaml file note a directory containing bounding box data files specifying the positive training samples must be present this can be created using the annotation tool the imagenet downloader or by converting the labels from the kitti dataset to annotate images with bounding boxes python m cardetection detection annotation img dir info file display width 1500 to generate and save samples based on given annotation info files python m cardetection detection save samples note uses annotation located in the bbinfo directory specified in template yaml bounding box data will be loaded from all files with names matching bbinfo dat and exclusion data will be loaded from all files matching exclusion dat to download images from imagenet python m download synset images with info download dir downloads all images with bounding boxes from the synsets listed in parent words yaml to the given directory to save kitti labels for training python m cardetection detection kitti to generate a synthetic dataset python m cardetection detection syntheticdataset tensorflow note at present the stable versions of tensorflow 0 6 0 don t work with the code for this project so it was installed from the source on the current master branch ie commit 6671d58ff9c60c724582e4e7a4ddaad0a0acda5a see the instructions in the web app deployment section deploy web app to a digitalocean ubuntu 1510 x64 server for how to build tensorflow from source on ubuntu the current tensorflow model used is based on the following it s nearly identical to the cifar 10 example https github com tensorflow tensorflow blob master tensorflow models image mnist convolutional py https github com aymericdamien tensorflow examples blob master examples 3 20 20neural 20networks alexnet py https www tensorflow org versions r0 7 tutorials deep cnn index html https www tensorflow org versions r0 7 tutorials mnist pros index html deep mnist for experts it was trained with the following setting at a rate of 58 examples second trained for 1550 steps pos frac 0 2 hard negative frac 0 05 exclusion frac 0 05 batch size 1000 learning rate 1e 4 to build and run tensorboard cd tensorflow repository directory e g users mitchell code tensorflow bazel build tensorflow tensorboard tensorboard bazel bin tensorflow tensorboard tensorboard logdir projects car detection output cnn train then visit the displayed url in a web browser other scripts saveclusters py clusters samples using hog features and k means then saves an average edge image and a mosaic of 100 samples for each cluster aspect histogram py saves histograms of angles and aspect ratios of samples in the kitti dataset useful tools line profiler for profiling https github com rkern line profiler | ai |
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Backend-template | kickstart backend project overview this is a backend project designed to jumpstart your development process with pre configured features and essential tools it s built using fastify and typescript and comes with various out of the box functionalities including user authentication security measures cors configuration environment variable management and automatic api documentation generation using openapi 3 schemas features fastify this project is built on the fastify framework known for its speed and performance typescript it s written entirely in typescript providing type safety and enhanced development experience user authentication user authentication and registration are seamlessly integrated through the use of the authorizer library security security is a top priority the project includes the fastify helmet plugin to enhance security by setting various http headers cors configuration cross origin resource sharing cors is pre configured to ensure secure interaction with client side applications environment variables the project loads environment variables from env files making it easy to configure for different environments database connectivity it connects to both mongodb and redis databases redis is used for rate limiting functionality automatic documentation api documentation is generated automatically using openapi 3 schemas helping you keep your api documentation up to date effortlessly getting started to kickstart your project development follow these steps deploy authorizer before you start deploy an instance of the authorizer on any cloud service the authorizer instance will instantiate a redis server and a postgresql server along with the authorizer itself database configuration delete the postgresql instance created by the authorizer and set up a mongodb instance to match your needs configure the environment variables to match your new mongodb instance environment variables set up environment variables configure your cloud services to match the new mongodb instance configure other environment variables e g api keys secrets as needed installation clone this repository install dependencies using npm install or yarn install development start the development server using npm run dev or yarn dev documentation explore the automatically generated api documentation by accessing the api swagger documentationez endpoint build and deployment when ready for production build the project using npm run build or yarn build deploy the project to your preferred hosting platform environment variables make sure to set up the following environment variables database name your mongodb database name mongodb url url for your mongodb database redis url url for your redis database cookie secret a secret to sign your cookies they will contain users tokens port the port in which your backend will listen node env dev or production self explanatory authorizer url the url for your authorizer instance authorizer client id the client id of your authorizer instance domain either localhost for dev purposes or your actual domain where you run your fronted contributing feel free to contribute to this project by opening issues or submitting pull requests your contributions are highly appreciated license this project is licensed under the mit license | server |
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isanlp | license mit https img shields io badge license mit yellow svg https github com iinemo isanlp srl framebank blob master license python 3 9 https img shields io badge python 3 9 green svg description isanlp is a python 3 library that encompasses several open source natural language processing tools for english and russian and provides a framework for running them locally as a single pipeline or in a distributed environment via rpc it also provides an easy to deploy docker container inemo isanlp for starting various types of workers warning since version 0 0 5 compatibility is broken with old containers when installing new version of the library you need to pull new containers or use 0 0 1 version of the library old containers are also tagged as 0 0 1 getting started 1 installing the library pip install git https github com iinemo isanlp git 2 optional starting docker container with installed dependencies and models docker run ti rm p 3333 3333 inemo isanlp basic usage python from isanlp processor remote import processorremote ppl processorremote host localhost port 3333 pipeline name default text ru annotations ppl text ru print annotations included components basic text analyzers fire indicates up to date recommended modules module tokenizing lemma br tizing pos br tagging morpho br logy ud syntax ner path razdel https github com natasha razdel ru isanlp processor razdel nltk https www nltk org en ru en en ru en isanlp en pipeline default isanlp ru pipeline default mystem https yandex ru dev mystem ru ru ru isanlp ru processor mystem polyglot https github com abosamoor polyglot en ru ru isanlp processor polyglot syntaxnet https github com iinemo syntaxnet wrapper en ru en ru docker pull inemo syntaxnet rus isanlp processor syntaxnet remote udpipe 2 5 https ufal mff cuni cz udpipe en ru en ru en ru en ru en ru isanlp processor udpipe br or docker pull tchewik isanlp udpipe isanlp processor remote grameval2020 br qbic https github com tchewik isanlp qbic ru ru ru ru docker pull tchewik isanlp qbic isanlp processor remote deeppavlov joint parser http docs deeppavlov ai en master features models syntaxparser html joint model usage ru ru ru ru isanlp processor deepavlov syntax spacy https spacy io usage models br 21 languages en ru en ru en ru en ru en ru en ru isanlp processor spacy br or docker pull tchewik isanlp spacy ru en isanlp processor remote core nlp processors semantic role labeling isanlp srl framebank https github com iinemo isanlp srl framebank russian semantic role labeler srl based on framebank and neural network models deep srl https hub docker com r inemo isanlp deep srl parser as a standalone docker container semantic role labeling for english discourse parsing isanlp rst https github com tchewik isanlp rst rst style discourse parser for russian based on neural network models coreference resolution corefhd https github com tchewik corefhd coreference resolution for russian trained on rucoco 23 dataset additional modules preprocessors polyglot language detector conll converters postag converters maltparser dependency parsing for now without runtime and models maltparser conll 2008 dependency parsing with a runtime and a model for english as a standalone docker container to be included english russian advanced neural network named entity recognition english russian sentiment analysis usage common usage the most common usage consists in constructing a pipeline of processors with pipelinecommon class for example the following pipeline performs tokenization sentence splitting two types of morphological mystem and deeppavlov and syntax deeppavlov analysis locally without remote containers python from isanlp import pipelinecommon from isanlp simple text preprocessor import simpletextpreprocessor from isanlp processor razdel import processorrazdel from isanlp ru processor mystem import processormystem from isanlp ru converter mystem to ud import convertermystemtoud from isanlp processor deeppavlov syntax import processordeeppavlovsyntax ppl pipelinecommon simpletextpreprocessor text text text processorrazdel text tokens tokens sentences sentences processormystem tokens sentences postag mystem postag convertermystemtoud mystem postag morph mystem morph postag mystem postag processordeeppavlovsyntax tokens sentences lemma lemma postag postag syntax dep tree syntax dep tree the pipeline contains a list of processors objects that perform separate language processing tasks the result of the pipeline execution is a dictionary of facets different types of annotations extracted from the text by processors the dictionary of annotations is stored inside the pipeline and filled up by processors processors get their input parameters from the annotation dictionary and save results into the dictionary the parameters of processors are specified in a tuple during pipeline construction pipelinecommon procobject list of input parameters dictionary of output results you also should specify the label which would be used to save your results in a pipeline annotation dictionary if you do not provide a name for a result annotation it would be dropped from further processing processors can overwrite annotations aquired from other processors to avoid overwriting just drop the result annotations pipelines also can include other pipelines and remote processors python from isanlp pipeline common import pipelinecommon from isanlp processor remote import processorremote from isanlp processor syntaxnet remote import processorsyntaxnetremote ppl pipelinecommon processorremote host some host port 8207 text tokens tokens sentences sentences lemma lemma postag postag morph morph processorsyntaxnetremote host other host port 7678 sentences syntax dep tree syntax dep tree conditional execution it is sometimes necessary to run different texts through different processors for instance depending on the text s language or length this is possible with the isanlp pipeline conditional pipelineconditional only texts longer than one sentence will be passed to the discourse parser in the following example python from isanlp pipeline conditional import pipelineconditional class dummyprocessor returns whatever we ll say def init self output self output output def call self args kwargs return self output address syntax hostname 3334 address rst hostname 3335 condition lambda te to sentences po mo le sy len sentences 1 rst pipeline variants 0 dummyprocessor output rst 1 processorremote address rst 0 address rst 1 pipeline name default ppl pipelinecommon processorrazdel text tokens tokens sentences sentences processorremote address syntax 0 address syntax 1 0 tokens sentences lemma lemma syntax dep tree syntax dep tree postag ud postag processormystem tokens sentences postag postag convertermystemtoud postag morph morph postag postag pipelineconditional condition rst pipeline variants text tokens sentences postag morph lemma syntax dep tree rst rst ppl rst ppl rst isanlp annotation rst discourseunit at 0x7f229bccd490 data structures the pipeline returns the results in a dictionary with keys that were provided in the parameters of processors if processor returns none results are ommited available keys values structures lang str iso code of detected language lemma list list of lists of string lemma annotations for each sentence the first list corresponds to sentences the second corresponds to annotations inside a sentence morph list list of lists of dictionary objects where the keys are the morphological features available for the current word postag list list of lists of string pos tags for each sentence sentences list list of isanlp annotation sentence objects each sentence contains begin int number of the first token in a sentence end int number of the last token in a sentence 1 syntax dep tree list list of lists of isanlp annotation wordsynt objects wordsynt objects mean nodes in syntax dependency tree and have two members parent int number of the parent token link name str name of the link between this word and it s parent tokens list list of isanlp annotation token objects each token contains text str as represented in text begin int position of the first symbol in the text end int position of the last symbol in the text 1 entities list list of isanlp annotation taggedspan objects each taggedspan contains tag str named entity tag begin int position of the first token in the text end int position in the last token in the text 1 docker worker the library provides a docker container inemo isanlp that contains all required packages and models to run a natural language pipeline you can invoke rpc server grpc with a default pipeline using the following command docker run rm p 3333 3333 inemo isanlp you can also run a custom processor instead the container using the simple command docker run rm p 3333 3333 v some repo isanlp start py m custom module a pipeline object additional modules for isanlp the isanlp library provides several core routines processors and models that do not need many dependencies however the isanlp framework is significantly broader it encompasses many other easy to use packages for various types of nlp currently they include 1 isanlp srl framebank https github com iinemo isanlp srl framebank the neural based srl parser for russian based on well known framebank https github com olesar framebank corpus the package provides the python 3 library for final users trained models shelmanov and devyatkin a docker container inemo isanlp srl framebank with ready to use nlp service and a playground for training new deep learning models jupyter notebook 2 isanlp rst https github com tchewik isanlp rst the neural based rst discourse parser for russian provides a docker container with trained models and open source code 2 isanlp deep srl https github com iinemo isanlp deep srl the docker container inemo isanlp deep srl https hub docker com r inemo isanlp deep srl with ready to use srl service for english the parser and models is created by deep srl project https github com luheng deep srl and adopted for isanlp framework 3 isanlp parser conll2008 https github com iinemo isanlp parser conll2008 the docker container inemo isanlp parser conll2008 https hub docker com r inemo isanlp parser conll2008 with ready to use dependency parsing service for english the parser is based on maltparser and conll 2008 2009 syntactic annotation semantically rich the model is developed by surdenau et al http www surdeanu info mihai ensemble 4 docker syntaxnet https github com iinemo docker syntaxnet the docker containers with google s syntaxnet https github com tensorflow models tree master research syntaxnet for english https hub docker com r inemo syntaxnet eng russian https hub docker com r inemo syntaxnet rus and italian https hub docker com r inemo syntaxnet ita the containers provides easy to use grpc service for dependency parsing pos tagging and morphology analysis 5 exactus sem vectors https gitlab com semvectors doc enc semantic vectors for searching over long documents roadmap 1 expand documentation 2 automatic tests 3 sentiment analysis for english and russian 4 spacy wrapper | ai |
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oxford-llms-workshop | oxford large language models for social science workshop as artificial intelligence and language models continue to revolutionize various sectors we are thrilled to invite early career social scientists to our 3 day workshop where we aim to bridge the gap and unlock the transformative potential of language models in the realm of social sciences team maksim zubok workshop organiser https www linkedin com in maksim zubok 160572232 dphil candidate in politics at oxford university nuffield college elena voita lecturer https www linkedin com in elena voita research scientist at fair meta ai ilya boytsov seminarist https www linkedin com in ieboytsov nlp lead ar wayfair content intelligence team workshop program day 1 fundamentals of large language models 9 00 am 12 00 pm lecture during this session you ll gain a comprehensive understanding of the fundamental concepts underlying language models we ll explore the evolutionary journey from rule based systems to modern transformers based models which are the state of the art technology in natural language processing nlp 12 00 pm 1 00 pm lunch break 1 00 pm 4 00 pm coding tutorial introduction to transformer models get hands on experience implementing and utilizing transformer models for various nlp tasks in social sciences practical exercises and coding examples will be provided day 2 model interpretation and debiasing 9 00 am 12 00 pm lecture learn techniques for interpreting language model outputs including attention visualization and probing we ll also address model bias and explore strategies to detect and mitigate bias in language models ensuring unbiased social science research 12 00 pm 1 00 pm lunch break 1 00 pm 4 00 pm coding tutorial interpreting and debiasing language models apply the concepts covered in the lecture to interpret and debias language models hands on experience with various interpretation techniques and practical exercises will be provided day 3 recent developments large language models and alignment 9 00 am 12 00 pm lecture discover recent advancements in language models including large scale models and reinforcement learning from human feedback rlhf understand the creation process of instructgpt models like the popular chatgpt and explore their applications in social science research 12 00 pm 1 00 pm lunch break 1 00 pm 4 00 pm coding tutorial advanced techniques with large language models and alignment explore advanced techniques with large language models and rlhf gain hands on experience with fine tuning large scale models using rlhf methods on your own research questions and datasets 4 00 pm 6 pm coding challenge and awards ceremony team up to tackle a challenging research problem showcase your creativity efficiency and innovation in solving the task using language models prizes will be awarded for the most outstanding solutions and innovations this is your chance to test your skills build networks and delve deep into the thrilling world of language models location department of sociology oxford university 42 park end st oxford ox1 1jd limited seats available apply now to secure your spot application link https forms gle bvymkghepge9y5ht6 for inquiries contact maksim zubok nuffield ox ac uk mailto maksim zubok nuffield ox ac uk languagemodels socialscience aiinresearch nlp workshop join us at the forefront of innovation in social science research let s harness the power of language models together and revolutionize the future | ai |
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embench-iot | embench x2122 open benchmarks for embedded platforms this repository contains the embench x2122 free and open source benchmark suite these benchmarks are designed to test the performance of deeply embedded systems as such they assume the presence of no os minimal c library support and in particular no output stream the rationale behind this benchmark is described in embench an evolving benchmark suite for embedded iot computers from an academic industrial cooperative towards the long overdue and deserved demise of dhrystone by david patterson jeremy bennett palmer dabbelt cesare garlati g s madhusudan and trevor mudge see https tmt knect365 com risc v workshop zurich agenda 2 software embench tm a free benchmark suite for embedded computing from an academic industry cooperative towards the long overdue and deserved demise of dhrystone the benchmarks are largely derived from the bristol embecosm embedded benchmark suite beebs see https beebs mageec org which in turn draws its material from various earlier projects a full description and user manual is in the doc directory doc readme md stable benchmark versions the following git tags may be used to select the version of the repository for a stable release embench 0 5 embench 1 0 using the benchmarks the benchmarks can be used to yield a single consistent score for the performance of a platform and its compiler tool chain the mechanism for this is described in the user manual doc readme md the benchmarks should all compile to fit in 64kb of program space and use no more than 64kb of ram note an earlier version tried to limit ram to 16kb but this proved too restrictive the measurement of execution performance is designed to use hot caches thus each benchmark executes its entire code several times before starting a timing run execution runs are scaled to take approximately 4 second of cpu time this is large enough to be accurately measured yet means all 19 benchmarks including cache warm up can be run in a few minutes to facilitate execution on machines of different performance the tests are scaled by the clock speed of the processor the benchmarks are designed to be run on either real or simulated hardware however for meaningful execution performance results any simulated hardware must be strictly cycle accurate structure of the repository the top level directory contains python scripts to build and execute the benchmarks the following are the key top level directories config config containing a directory for each architecture supported and within that directory subdirectories for board and cpu descriptions configuation data can be provided for individual cpus and individual boards note the structure of the config config directory is proving overly complex yet inflexible it is likely it will be flattened in a future release of the scripts doc doc the user manual for embench src src the source for the benchmarks one directory per benchmark support support the generic wrapper code for benchmarks including substitutes for some library and emulation functions pylib pylib support code for the python scripts licensing embench is licensed under the gnu general public license version 3 gpl3 see the copying file for details some individual benchmarks are also available under different licenses see the comments in the individual source files for details the code base is openchain compliant with spdx license identifiers provided throughout | server |
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bamf | bamf license https img shields io badge license gnu brightgreen svg https github com malwaredllc bamf license version https img shields io badge version 0 1 2 lightgrey svg https github com malwaredllc bamf disclaimer this project should be used for authorized testing and educational purposes only bamf is an open source tool designed to leverage shodan a search engine for the internet of things to discover routers vulnerable to cve 2013 6026 https nvd nist gov vuln detail cve 2013 6026 commonly known as joel s backdoor a severe vulnerability allowing unauthenticated access to the administration panel of many routers made by d link one of the world s largest manufacturers of routers for home and business installation 1 download or clone the repository git clone https github com malwaredllc bamf 2 install the required python packages pip install r bamf requirements txt 3 create a free shodan account at https account shodan io register 4 configure bamf to use your shodan api key python bamf py shodan api usage use the search command to search the internet for potential use the scan command to scan the target routers for backdoors use the map command to map the networks of devices connected to vulnerable routers use the targets command to view potential targets discovered this session use the backdoors command to view routers with a confirmed backdoor use the devices command to view all devices connected to vulnerable routers contact email security malwared com twitter twitter https img shields io twitter url http shields io svg style social https twitter com malwaredllc | backdoor pharmer router shodan | server |
eor-adafruit-ili9341 | ili9341 support for esp open rtos ili9341 support for esp open rtos eor using adafruit s arduino library since the library is mit licensed it s safe to include in eor the included demo program is among other things a wifi photo viewer you can upload images to howto assuming you have cloned the eor git and set your wifi credentials export eor root path to esp open rtos cd eor root git clone https github com kanflo eor arduino compat git extras arduino compat git clone https github com kanflo eor adafruit ili9341 git extras ili9341 git clone https github com kanflo eor cli git extras cli cd extras ili9341 example make j8 make flash check the uart output for the ip address and then try cat sunset bmp nc w1 ip address 80 or try help in the uart command line interface note that image upload using nc should take less than 1s if it takes longer try adding the following line to lwip include lwipopts h and rebuild define tcp wnd tcp mss 2 see eor issue 151 https github com superhouse esp open rtos issues 151 for further information changes compared to the adafruit master the changes to the original adafruit source files are renamed c cpp h hpp changed spi spic changed write to writec as write is an lwip macro inclusion of eor arduino compat hpp the ili9341 and gfx libraries are copyright c 2013 adafruit industries all rights reserved | os |
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CVFundamentals | cvfundamentals computer vision fundamentals github https github com anjiang2016 cvfundamentals week1 week2 cv week2 pipeline 1 2 3 4 0 1 10 0 1 2 3 4 5 6 7 8 9 2 10 x 3 f x y x 0 y f x 0 x 1 y f x 1 x 2 y f x 2 x 3 y f x 3 x 4 y f x 4 x 5 y f x 5 x 6 y f x 6 x 7 y f x 7 x 8 y f x 8 x 9 y f x 9 4 week2 recognize computer vision py week3 github https github com anjiang2016 cvfundamentals cv week3 pipeline 1 2 3 3 4 1 pytorch auto grad 1 10 0 1 2 3 4 5 6 7 8 9 2 10 x 3 f x y 4 pytorch auto grad x 0 y f x 0 x 9 y f x 9 week3 recognize computer vision linear model py week3 how to use auto grad py pytorch auto grad week3 data display ipynb week3 week2 ipynb week3 auto grad ipynb week3 auto grad ipynb week3 running jupyter pdf jupyter jupyter https zhuanlan zhihu com p 143919082 week4 github https github com anjiang2016 cvfundamentals cv week4 pipeline 1 2 3 4 5 6 svm hct66 dataset mnist 2 3 mnist 100 1 pytorch auto grad 2 epoch acc acc 1 mnist https github com anjiang2016 cvfundamentals blob master week4 mnist readme md 2 mnist x opencv hog lbp 3 y 4 pytorch auto grad 60 60 week4 assignment week03 answer py week3 week4 recognize computer vision nonlinear model py week4 use 2class on multiclass py htc66 week4 use 2class on multiclass sigmoid py htc66 w week4 mnist readme md mnist week4 mnist use 2class on multicalss py mnist week4 mnist bp py bp week4 homework dataset hct1000 | ai |
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ITI-ExaminantionSystem | iti examinantionsystem information technology institute official examination system control and monitoring system include all system entities and possible interactions | server |
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vision_tutorial | vision tutorials robomaster opencv api email pr issue | computer-vision deep-learning image-processing robomaster tutorials | ai |
cs231b | cs 231b cutting edge of computer vision spring 2014 15 project 1 interactive image segmentation with grabcut project 2 tracking with tld project 3 detection with r cnn | ai |
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diskspd | diskspd diskspd is a storage performance tool from the windows windows server and cloud server infrastructure engineering teams at microsoft please visit https github com microsoft diskspd wiki for updated documentation in addition to the tool itself this repository hosts measurement frameworks which utilize diskspd the initial example is vm fleet https github com microsoft diskspd blob master frameworks vmfleet used for windows server hyper converged environments with storage spaces direct this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments releases the releases https github com microsoft diskspd releases page includes pre compiled binaries zip and source code for the most current releases of the diskspd tool the latest update to diskspd can always be downloaded from https github com microsoft diskspd releases latest download diskspd zip aka https aka ms getdiskspd what s new diskspd diskspd 2 1 7 1 2021 new g n i form allowing throughput limit specification in units of iops per specified blocksize new rs pct to specify mixed random sequential operation pct random geometric distribution of run lengths new rd distribution to specify non uniform io distributions across target pct by target percentage abs by absolute offset new rp text xml to show specified parameter set in indicated profile output form works with x xml profiles and conventional command line xml results profiles are now indented for ease of review text result output updates now shows values in size units k m g and now tib to two decimals thread stride no longer shown unless specified f o threadpool parameters shown xml profiles can now be built more generically xml profiles can be stated in terms of templated target names 1 2 replaced in order from command line invocation the command line now allows options alongside x v z r and w d c along with template target specs diskspd 2 0 21a 9 21 2018 added support for memory mapped i o new sm option to enable memory mapped i o new n vni option to specify flush options for memory mapped i o added support for providing event tracing for windows etw events included a windows performance recorder wpr profile to enable etw tracing added system information to the resultparser output diskspd 2 0 20a 2 28 2018 changes that may require rebaselining of results new random number generator that may show an observable decreased cost switched to 512 byte aligned buffers with the z option to increase performance new o option for specifying the number of outstanding io requests per thread new zr option for per io randomization of write buffer content xml adds a new threadtarget element to support target weighting schemes enhanced statistics captured from iops data added support for validating xml profiles using an in built xsd added support for handling raw volumes updated cpu statistics to work on 64 core systems updated calculation and accuracy of cpu statistics re enable support for etw statistics diskspd 2 0 18a 5 31 2016 update example to use sh v deprecated h fix operation on volumes on gpt partitioned media driveletter fix io priority hint to proper stack alignment if not 8 byte will fail use ib notation to clarify that text result output is in 2 n units kib mib gib diskspd 2 0 17a 5 01 2016 s is expanded to control write through independent of os software cache among other things this allows buffered write through to be specified sbw xml adds a new writethrough element to specify write through xml disableallcache is no longer emitted still parsed though in favor or writethrough and disableoscache text output os software cache and write through state are now documented separately adjacent lines latency histogram now reports to 9 nines one part in one billion in both text and xml output error message added for failure to open write content source file z size file vm fleet vm fleet is a performance characterization and analyst framework for exploring the storage capabilities of windows server hyper converged environments with storage spaces direct vm fleet 2 0 2 2 12 1 2021 fix cluster remoting issue during new fleet caused by 2 0 2 1 work use timestamped logging in new fleet simplify and de colorize default output vm fleet 2 0 2 1 11 9 2021 fix cluster remoting issues in move fleet and get fleetdatadiskestimate fix timing issue with start fleetsweep always start from fleet pause to avoid triggering free run use uniqueness to guarantee start fleetsweep runs profile in case of repeat vm fleet 2 0 2 11 2 2021 windows server 2019 rs5 host operation now confirmed supported read cache warmup for hdd capacity systems should now be faster set fleetpause will wait for vm responses before completion by default see timeout several minor fixes including disable windows recovery console in fleet vms fix show fleet iops view now aggregates all vm disk devices fix clean up leaked conflicting data collectors and blg automatically vm fleet 2 0 9 22 2021 major release and rewrite as a first class powershell module original script based vm fleet remains available at frameworks vmfleet1 0 see the documentation https github com microsoft diskspd wiki vmfleet in the wiki source code the source code for diskspd is hosted on github at https github com microsoft diskspd any issues with diskspd can be reported using the following link https github com microsoft diskspd issues | cloud |
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Scorex | scorex 2 the modular blockchain framework build status https travis ci org scorexfoundation scorex svg branch master https travis ci org scorexfoundation scorex coverage status https coveralls io repos github scorexfoundation scorex badge svg branch scoverage reports https coveralls io github scorexfoundation scorex branch scoverage reports scorex and scorex 2 scorex 2 is modular blockchain framework is scala language which allows for free and limitless experimentation with a wide variety of designs it is a complete rewrite of scorex framework which can be found at https github com input output hk scorex https github com input output hk scorex motivation if you have a new design for a blockchain system there are few options available in regards with an implementation you can fork codebase of a bitcoin or ethereum client however such clients are optimized towards concrete protocol thus implementing something different would be a cumbersome task there are some modular frameworks such as scorex where you can change consensus layer or transactional layer or both still these modules have concrete interfaces so for many designs more low level and abstract approach was needed we have read a lot of research papers to make scorex 2 supporting their implementations its abstract core allows for implementing a broad range of systems including ones with multiple types of blocks and non linear history features compact functional code modular design with fine granularity scala language asynchronous networking layer on top of tcp json api cryptographic primitives externalized into separate scrypto framework https github com input output hk scrypto some examples provided including one working in production documentation and communication please join maillist at https groups io g scorex dev https groups io g scorex dev there is tutorial in progress available at https github com scorexfoundation scorextutorial https github com scorexfoundation scorextutorial examples there are two examples of blockchain systems built with scorex implementation of twinscoin a hybrid proof of work proof of stake cryptocurrency is provided in this repository please check dedicated readme examples readme md treasurycoin https github com input output hk treasurycoin is an experimental implementation of the perspective treasury system described in a treasury system for cryptocurrencies enabling better collaborative intelligence https eprint iacr org 2018 435 pdf ergo platform https github com ergoplatform ergo a proof of work cryptocurrency and financial contracts platform with support for stateless nodes different operating regimes with up to 4 possible sections in a block etc development plans final 1 0 release of scorex 2 is not done but near currently we re polishing and auditing the codebase towards the release then we will consider further plans possibly including improved networking layer support for scala 3 more examples possibly including non linear systems such as dags contributions contributions are welcome please take a look into issues https github com scorexfoundation scorextutorial issues testing codebase is still not perfect at the moment so writing a test is not just good for start but useful for the product as well new examples would be very helpful as well release to publish release version to sonatype make a tag with version number vx y z used by sbt dynver to set version in build sbt use the new tag to make a github release which triggers release yml github workflows release yml workflow and publishes release version to sonatype | blockchain cryptocurrency scala | blockchain |
cloud-collection | guide to the cloud welcome to the cloud collection an ongoing curated list of awesome frameworks important books and articles talks and videos libraries learning tutorials cloud best practices and technical resources about the cloud in software engineering thanks to our daily readers and contributors the goal is to build a categorized community driven collection of very well known resources sharing suggestions and contributions are always welcome table of contents introduction introduction what is the cloud the cloud https azure microsoft com en us resources cloud computing dictionary what is the cloud is not a physical entity but instead is a vast network of remote servers around the globe which are hooked together and meant to operate as a single ecosystem cloudnetwork https github com exajobs cloud collection blob main img cloud network jpg what are cloud servers a cloud server https www ibm com cloud learn cloud server is powerful physical or virtual infrastructure that performs application and information processing storage cloud servers are created using virtualization software to divide a physical bare metal server into multiple virtual servers cloudsevers https github com exajobs cloud collection blob main img 1200px cloud computing png png these servers are designed to either store and manage data run applications or deliver content or a service such as streaming videos web mail office productivity software or social media instead of accessing files and data from a local or personal computer you are accessing them online from any internet capable device the information will be available anywhere you go and anytime you need it key features of cloud servers computing infrastructure that can be physical bare metal virtual or a mix of the two depending on use case has all the capabilities of an on premises server enables users to process intensive workloads and store large volumes of information automated services are accessed on demand through an api gives users the choice of monthly or as you go payment users can opt for a shared hosting plan that scales depending on needs why cloud servers cost effectiveness with cloud servers organizations only pay for what they need and reduce the expense that comes with maintaining server hardware scalability users can scale computing and storage resources to meet changing needs this is particularly helpful for organizations with fluctuating needs integration an organization s cloud servers are networked to ensure uninterrupted communication and fast deployment a single pane enables complete control considerations cloudserversconsideration https github com exajobs cloud collection blob main img bare metal server vs cloud servers jpg virtual servers vs physical servers physical bare metal servers are best for data intensive workloads virtual servers are better for highly variable workloads virtualization cloud servers can be physical or virtual virtualization software options include vmware parallels and hyper v customization physical servers have numerous customization options such as more processing power additional ram and backup power security security options for cloud servers include firewalls anti virus software monitoring and host intrusion protection license mit license cc https creativecommons org licenses by 4 0 license a rel license href http creativecommons org licenses by 4 0 img alt creative commons license style border width 0 src https i creativecommons org l by 4 0 88x31 png a br this work is licensed under a a rel license href http creativecommons org licenses by 4 0 creative commons attribution 4 0 international license a back to top guide to the cloud | cloud cloud-foundry cloud-migration cloudmigration cloudsecurity cloudserver cloudservices | cloud |
HuggingFaceGuidedTourForMac | huggingface and deep learning guided tour for macs with apple silicon version 2 a guided tour on how to install optimized tensorflow and pytorch on apple silicon mac and how to use huggingface large language models for your own experiments we will perform the following steps install homebrew install tensorflow with and apple s metal pluggable metal driver optimizations install pytorch with mps metal performance shaders support install jupyter lab to run notebooks install huggingface and run some pre trained language models using transformers and just a few lines of code within jupyter lab note previous versions of this guide used conda and specific conda chanels to install custom version of pytorch and tensorflow and its support software this kind of special versions are no longer required the recommendation is to uninstall conda and use python s venv to install the required software see below at the end of this readme for uninstallation instructions for conda 1 preparations 1 1 install homebrew if you haven t done so go to https brew sh and follow the instructions to install homebrew once done open a terminal and type brew version to check that it is installed correctly now use brew to install more recent versions of python and git bash brew install python git to update at a later time bash brew upgrade 1 2 test project now clone this project as a test project bash git clone https github com domschl huggingfaceguidedtourformac now create a python environment for this project and activate it bash python m venv huggingfaceguidedtourformac this adds the files required for the virtual python environment to the project we just cloned cd huggingfaceguidedtourformac source bin activate now the directory huggingfaceguidedtourformac contains the content of the github repository e g 00 systemcheck ipynb and the the files for the virtual env e g bin folder content https github com domschl huggingfaceguidedtourformac blob main resources projectfolder png 1 3 when you done with your project do deactivate this virtual environment simply use bash deactivate to re activate it enter the directory huggingfaceguidedtourformac and use bash source bin activate note see https docs python org 3 tutorial venv html for more information about python virtual environments 2 install tensorflow note since tensorflow version 2 13 installation has significantly simplified since the standard tensorflow can be used with addition of the pluggable metal driver make sure that your virtual environment is active with pip v uppercase v this should show a path for pip within your project your path huggingfaceguidedtourformac lib python3 11 site packages pip python 3 11 following https developer apple com metal tensorflow plugin we will install tensorflow with pip within our venv bash pip install u tensorflow tensorflow metal 2 1 quick test to test that tensorflow is installed correctly open a terminal type python and within the python shell enter python import tensorflow as tf tf config list physical devices gpu you should see something like physicaldevice name physical device gpu 0 device type gpu 3 install pytorch following https pytorch org we will install pytorch with pip the current version 2 in order to get mps metal performance shaders support within pytorch which offers significant performance advantage on apple silicon to install pytorch into the venv bash pip install u torch torchvision torchaudio 3 1 quick test to test that pytorch is installed correctly and mps metal performance shaders are available open a terminal type python and within the python shell enter python import torch check if mps is available torch backends mps is available this should return true 4 jupyter lab at this point your apple silicon mac should be ready to run tensorflow and pytorch with hardware acceleration support using the apple metal framework to test this you can use jupyter lab to run some notebooks to install jupyter lab type bash pip install u jupyterlab ipywidgets to start jupyter lab type bash jupyter lab this should open a browser window with jupyter lab running you can then create a new python notebook and run some code to test that tensorflow and pytorch are working correctly resources jupyterlab png python import tensorflow as tf import torch print tensorflow version tf version print pytorch version torch version if this completed successful your mac is now ready for deep learning experiments 5 huggingface huggingface is a great resource for nlp and deep learning experiments it provides a large number of pre trained language models and a simple api to use them it will allow us to quickly get started with deep learning experiments 5 1 install transformers from the huggingface installation instructions https huggingface co docs transformers installation we use pip to install transformers bash pip install u transformers note when experimenting with huggingface you will download large models that will be stored in your home directory at cache huggingface hub you can remove these models at any time by deleting this directory or parts of it s content 6 experiments 6 1 simple sentiment analysis within the directory huggingfaceguidedtourformac start jupyter lab and load the 00 systemcheck ipynb notebook use shift enter to run the notebook s cells your should see something like this resources huggingface transformers png if you ve received a label classification of positive with a score of 0 99 then you are ready to start experimenting with huggingface note you ll see that the huggingface libraries are downloading all sorts of large binary blobs containing the trained model data that data is stored in your home directory at cache huggingface hub you can remove these models at any time by deleting this directory or parts of it s content 6 2 minimal chat bot you can open the notebook 01 chatbot ipynb https github com domschl huggingfaceguidedtourformac blob main 01 chatbot ipynb to try out a very simple chatbot on your mac the python code used is python import torch from transformers import automodelforcausallm autotokenizer from transformers utils import logging disable warnings about padding side that cannot be rectified with current software logging set verbosity error model names microsoft dialogpt small microsoft dialogpt medium microsoft dialogpt large use model index 1 change 0 small model 1 medium 2 large model requires most resources model name model names use model index tokenizer autotokenizer from pretrained model name padding side left model automodelforcausallm from pretrained model name the chat function received a user input and chat history and returns the model s reply and chat history def reply input text history none encode the new user input add the eos token and return a tensor in pytorch new user input ids tokenizer encode input text tokenizer eos token return tensors pt append the new user input tokens to the chat history bot input ids torch cat history new user input ids dim 1 if history is not none else new user input ids generated a response while limiting the total chat history to 1000 tokens chat history ids model generate bot input ids max length 1000 pad token id tokenizer eos token id pretty print last ouput tokens from bot return tokenizer decode chat history ids bot input ids shape 1 0 skip special tokens true chat history ids history none while true input text input if input text in bye quit exit break reply text history new reply input text history history history new if history shape 1 80 old shape history shape history history 80 print f history cut from old shape to history shape history text tokenizer decode history 0 print f current history history text print f d gpt reply text this shows a quite limited and repetitive chatbot using microsoft s dialogpt https huggingface co microsoft dialogpt medium text hey my name is mariama 21 how are you 3f models things to try by changing use model index between 0 2 you can select either a small medium or large language model to see the history that the model maintains you can uncomment the two history text related lines above to get rid of the downloaded models clean up cache huggingface hub missing stuff is automatically re downloaded when needed conda uninstallation notes note this paragraph is to uninstall conda that was used in older versions of this guide brew uninstall miniconda additional modifications are all of them are inactive once miniconda is removed condarc list of channels and conda zshrc or bashrc for the setup of path and environment after using hugginface models large model binary blobs may reside at cache huggingface hub simply remove the directory changes 2023 09 25 guide version 2 0 switched from conda to pip and venv for latest versions of tensorflow 2 13 pytorch 2 macos sonoma installation is now much simpler 2023 03 16 since pytorch v2 0 is now released the channel pytorch nightly can now be replaced by pytorch in the installation instructions the pytorch nightly channel is no longer needed for mps support | nlp pytorch chatbot howto-tutorial huggingface llm transformers | ai |
Mastering-Natural-Language-Processing-with-Python-Video | mastering natural language processing with python video this is the code repository for mastering natural language processing with python video https www packtpub com big data and business intelligence mastering natural language processing python video utm source github utm medium repository utm campaign 9781789618358 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the video course from start to finish about the video course natural language processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human computer interaction it provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning this course will give you expertise on how to employ various nlp tasks in python giving you an insight into the best practices when designing and building nlp based applications using python it will help you become an expert in no time and assist you in creating your own nlp projects using nltk you will sequentially be guided through applying machine learning tools to develop various models we ll give you clarity on how to create training data and how to implement major nlp applications such as named entity recognition question answering system discourse analysis transliteration word sense disambiguation information retrieval text summarization and anaphora resolution h2 what you will learn h2 div class book info will learn text ul li implement string matching algorithms and normalization techniques li implement statistical language modeling techniques li develop a search engine and implement pos tagging concepts and statistical modeling concepts involving the n gram approach li familiarize yourself with concepts such as the treebank construct cfg construction the cyk chart parsing algorithm and the earley chart parsing algorithm li develop an ner based system and understand and apply the concepts of semantic analysis li understand and implement the concepts of information retrieval and text summarization li develop a discourse analysis system and anaphora resolution based system li ul div instructions and navigation assumed knowledge to fully benefit from the coverage included in this course you will need br this course is for intermediate level developers in nlp with a reasonable knowledge level and understanding of python technical requirements this course has the following software requirements br for all the sections python 2 7 or 3 2 is used nltk 3 0 must be installed either on a 32 bit machine or 64 bit machine the operating system that is required is windows mac unix related products hands on natural language processing with python https www packtpub com big data and business intelligence hands natural language processing python utm source github utm medium repository utm campaign 9781789139495 natural language processing with python video https www packtpub com big data and business intelligence natural language processing python video utm source github utm medium repository utm campaign 9781787286085 next generation natural language processing with python video https www packtpub com big data and business intelligence next generation natural language processing python video utm source github utm medium repository utm campaign 9781789139938 | ai |
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ProgrammingBlockchain | assets programmingblockchain png click here for code examples https github com programmingblockchain programmingblockchaincodeexamples click here to read the book https programmingblockchain gitbooks io programmingblockchain content community gitter https badges gitter im metacosa nbitcoin svg https gitter im metacosa nbitcoin utm source badge utm medium badge utm campaign pr badge other languages indonesian read https programmingblockchain gitbooks io programmingblockchainindonesian content github https github com programmingblockchain programmingblockchain indonesian gitbook https programmingblockchain gitbook io programmingblockchainindonesian japanese read https programmingblockchain gitbooks io programmingblockchain japanese content github https github com programmingblockchain programmingblockchain japanese gitbook https www gitbook com book programmingblockchain programmingblockchain japanese korean github https github com programmingblockchain programmingblockchain korean quick feedback if you notice any mistakes and don t want to fix them yourself open an issue on the github page of the book https github com programmingblockchain programmingblockchain if you are reading this book with gitbook you can also create a quick inline comment by clicking the button for the paragraph how can i fix a typo aka quick contribution 1 find the book on github https github com programmingblockchain programmingblockchain 2 fork 3 edit file 4 make a pull request how can i write a new chapter aka extensive contribution 1 find the book on github https github com programmingblockchain programmingblockchain 2 fork 3 clone your fork 4 download and install gitbook editor https legacy gitbook com editor 5 open gitbook editor 6 select import and select the folder where you cloned your fork 7 edit book 8 save files and sync 9 make a pull request how can i write a new chapter aka extensive contribution for those literate in git and markdown 1 find the book on github https github com programmingblockchain programmingblockchain 2 fork 3 clone your fork to your computer 4 download and install the atom editor https atom io or your favorite editors notepad vim etc 5 open atom 6 select open folder and select the folder where you cloned your fork 7 edit the book with the help of the markdown preview package cmd ctrl shift m 8 save files 9 commit and push to your remote repo with your favorite git client github desktop git bash sourcetree etc 10 make a pull request enhancing your learning process making contributions while you are reading is a good way to learn faster if you have a hard time understanding something try to reword it and make a pull request for other readers you can also help fixing issues https github com programmingblockchain programmingblockchain issues protip for university students a good github profile is more valuable than a diploma in the job market how to feed us for every donation on this address you will appear on http n bitcoin ninja http n bitcoin ninja 1kf8kuvhk42xzgcmjf4lxz4wcl5wdl97pb assets bookqr png 1kf8kuvhk42xzgcmjf4lxz4wcl5wdl97pb https www smartbit com au address 1kf8kuvhk42xzgcmjf4lxz4wcl5wdl97pb links the book on github https github com programmingblockchain programmingblockchain the book on gitbook https www gitbook com book programmingblockchain programmingblockchain code examples on github https github com programmingblockchain programmingblockchaincodeexamples hall of the makers http n bitcoin ninja here are the true makers those that succeeded in completing the challenges of this book | bitcoin nbitcoin dotnet dotnet-core csharp | blockchain |
MOB-NYC-2 | mob nyc 1 mobile development with rudd | front_end |
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SoftwareEngineering2020 | cise s1 2020 team15 | server |
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Delphic | archive notice this is a great proof of concept for how to build a django powered app with streaming responses from a large language model i don t have the time to maintain it and it needs a number of upgrades to be production ready let me know if you re interested in taking over maintenance delphic docs images delphic header png a simple framework to use llamaindex to build and deploy llm agents that can be used to analyze and manipulate text data from documents built with cookiecutter django https img shields io badge built 20with cookiecutter 20django ff69b4 svg logo cookiecutter https github com cookiecutter cookiecutter django black code style https img shields io badge code 20style black 000000 svg https github com ambv black license mit getting setup word of caution note the initial release of delphic is based solely on openai s api we fully plan to support other large language models llms whether self hosted or powered by third party api at the moment however as of april 2023 open ai s api remains perhaps the most capable and easiest to deploy since this framework is based on llamaindex and is fully compatible with langchain it will be pretty easy to use other llms at the moment however your text will be processed with openai even if you re self hosting this tool if openai s terms of service present a problem for you we leave that to you to resolve we are not responsible for any issues arrising from your use of this tool and openai api getting started locally just run the application the fastest way to get up and running is to clone this repo and then deploy the application locally you will need docker and docker compose to follow these instructions digitalocean besides being an excellent cloud host has some of the easiest to follow instructions on setting these up please check them out here or go to the docker official instructions 1 first clone the repo commandline git clone 2 then change into the directory commandline cd delphic 3 don t forget to copy the sample env file to envs local you may need to be a super user use sudo depending on your desired location commandline mkdir p envs local cp a docs sample envs local frontend frontend cp a docs sample envs local django envs local cp a docs sample envs local postgres envs local 4 and next update your django configuration you ll probably want to edit postgres as well to give your database user a unique password to include your openai api key 5 then build the docker images commandline sudo docker compose profile fullstack f local yml build 6 finally to launch the application type commandline sudo docker compose profile fullstack f local yml up go to localhost 3000 to see the frontend i want to develop modify the frontend if you want to actively develop the frontend we suggest you not use the profile fullstack flag as every change will require a full container rebuild instead see the development environment development environment instead of step 5 above production deploy this assumes you want to make the application available to the internet at some kind of fully qualified domain like delphic opensource legal in order to do this you need to update a couple configurations todo insert documentation using the application setup users in order to actually use the application at the moment we intend to make it possible to share certain models with unauthenticated users you need a login you can use either a superuser or non superuser in either case someone needs to first create a superuser using the console why set up a django superuser a django superuser has all the permissions in the application and can manage all aspects of the system including creating modifying and deleting users collections and other data setting up a superuser allows you to fully control and manage the application warning disclaimer at the moment any user who is logged in will have full permissions we plan to implement the more precise roles based access control module we developed for opencontracts https github com jsv4 opencontracts but for now be aware that anyone with any type of login credentials can create and delete collections creating collections uses openai credits costs money first setup a django superuser 1 run the following command to create a superuser sudo docker compose f local yml run django python manage py createsuperuser 2 you will be prompted to provide a username email address and password for the superuser enter the required information second if desired setup additional users start your delphic application locally following the deployment instructions 1 visit the django admin interface by navigating to http localhost 8000 admin in your browser 2 log in with the superuser credentials you created earlier 3 click on the users link in the users section 4 click on the add user button in the top right corner 5 enter the required information for the new user such as username and password click save to create the user 6 to grant the new user additional permissions or make them a superuser click on their username in the user list scroll down to the permissions section and configure their permissions accordingly save your changes creating and querying a collection warning if you re using openai as your llm engine any collection interaction will use api credits cost money if you re using your own openai api key you ve also accepted their terms of service which may not be suitable for your use case please do your own diligence to access the question answering interface bring up the fullstack and go to http localhost 3000 https user images githubusercontent com 5049984 233236432 aa4980b6 a510 42f3 887a 81485c9644e6 mp4 development environment if you want to contribute to delphic or roll your own version you ll want to ensure you setup the development environment backend setup on the backend you ll need to have a working python environment to run the pre commit formatting checks you can use your system python interpreter but we recommend using pyenv and creating a virtual env based off of python 3 10 pre commit setup then in the root of your local repo run these commands pip install r requirements local txt pre commit install now when you stage your commits ou ar code formatting and style checks will run automatically running tests we have a basic test suite in tests you can run the tests by typing commandline sudo docker compose f local yml run django python manage py test frontend setup on the frontend we re using node v18 15 0 we assume you re using nvm we don t have any frontend tests yet sorry setup and launch node development server cd into the frontend directory install your frontend dependencies and start a development server note we assume you have nvm installed if you don t install it now commandline cd frontend nvm use npm install yarn yarn install typing yarn start will bring up your frontend development server at http localhost 3000 you still need to launch the backend in order for it to work properly run backend compose stack without fullstack profile flag launch the backend without the fullstack flag commandline sudo docker compose f local yml up | celery django generative-ai langchain llamaindex llm openai python gpt-4 gpt-3 | ai |
X-LLM | x llm x llm bootstrapping advanced large language models by treating multi modalities as foreign languages project page https x llm github io paper https arxiv org abs 2305 04160 a href https x llm github io img src https img shields io badge project page green a a href https arxiv org abs 2305 04160 img src https img shields io badge paper arxiv red a x llm converts multi modalities images speech videos into foreign languages using x2l interfaces and feed them into a large language model chatglm to accomplish a multimodal llm achieving impressive multimodal chat capabilities x llm is a general multimodal llm framework that allows us to incorporate various modalities of information into llms such as 1 non speech audios enabling the llm to have conversations about audios 2 terminal device status information enabling llm to control terminal devices and so on p align center img src images x llm png width 95 br x llm framework p x llm connects multiple pre trained single modal encoders such as vit g visual encoder and large language model chatglm using x2l interfaces we consider a three stage training procedure stage 1 converting multimodal information convert multimodal information into foreign languages through x2l interfaces only x2l interfaces are updated stage 2 aligning x2l representations with the llm inject foreign languages into llm only x2l interfaces are updated stage 3 integrating multiple modalities integrating multi modalities only the adapters in x2l interfaces are updated release 5 6 we will release the code as soon as possible contents install install dataset dataset training training evaluation evaluation performance performance install 1 creating conda environment bash conda create n lavis python 3 8 conda activate lavis 2 build from source bash git clone https github com phellonchen x llm git cd x llm pip install e dataset please see the readme data md https github com phellonchen x llm blob main readme data md for details training please see the readme train eval md https github com phellonchen x llm blob main readme train eval md for details evaluation please see the readme train eval md https github com phellonchen x llm blob main readme train eval md for details performance an evaluation dataset with 30 unseen images is constructed each image is assocaited with three types of instructions conversation detailed description and complex reasoning this leads to 90 new language image instructions on which we test x llm and gpt 4 and use chatgpt to rate their responses from score 1 to 10 the summed score and relative score per type is reported overall x llm achieves 84 5 relative score compared with gpt 4 indicating the effectinvess of the proposed method in multimodal settings p align center img src images pie x llm gpt4 png width 95 br p examples visual input example the forbidden city p align center img src images cmp forbidden png width 70 br p visual input example honor of kings p align center img src images cmp kings png width 70 br p acknowledgement chatglm https github com thudm chatglm 6b the codebase we built upon and our base model chatglm 6b that has the amazing chinese language capabilities blip2 https huggingface co docs transformers main model doc blip 2 the model architecture of x llm follows blip 2 don t forget to check this great open source work if you don t know it before if you find x llm useful for your your research and applications please cite using this bibtex article chen2023x title x llm bootstrapping advanced large language models by treating multi modalities as foreign languages author chen feilong and han minglun and zhao haozhi and zhang qingyang and shi jing and xu shuang and xu bo journal arxiv preprint arxiv 2305 04160 year 2023 | ai |
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primocss | primocss framework build status https travis ci org primocss primocss svg branch develop https travis ci org primocss primocss npm version https badge fury io js primocss svg http badge fury io js primocss downloads http img shields io npm dm primocss svg https www npmjs com package primocss bower version https badge fury io bo primocss svg https badge fury io bo primocss devdependency status https david dm org primocss primocss dev status svg https david dm org primocss primocss info devdependencies open source helpers https www codetriage com primocss primocss badges users svg https www codetriage com primocss primocss primocss framework a light weight and mobile friendly scss css framework for web ui development primo is a framework which provides the prime starting blocks for any frontend requirements without supplying out of the box solutions or mantras to follow it creates a solid architecture to kick start any project a great starting points all the styles and tools have been created as the perfect starting point for your project this isn t a full framework trying to impose design styles and mantras built with sass gulp using gulp to create tests and build steps developing the source code using sass and harnessing the powerful preprocessor for our css every view possible with a powerful responsive grid any design is possible for all devices and effectively scales from handhelds to desktops hotlink if you just need to include the latest compiled version of the primocss framework use our hosted version and fonts to begin your project html link href http primocss com build css primocss min css rel stylesheet media screen type text css now you re all done your site or project has primocss at it s core and you can start to build simple but effective responsive designs follow on reading for our basic html template basic template html doctype html html lang en dir ltr head meta charset utf 8 meta name viewport content width device width initial scale 1 shrink to fit no link href http primocss com build css primocss min css rel stylesheet media screen type text css head body h1 hello world h1 body html package manager installation primocss can be installed to any project using different package managers and obtain the scss source files to pull down the latest release use the following commands with npm yarn or bower npm bash npm install primocss save dev yarn bash yarn add primocss bower bash bower install primocss save dev local development if you would like to help and improve this project in anyway or use the project and develop on it locally follow these commands to help start by downloading via github https github com primocss primocss archive master zip or git cloning the project git clone git github com primocss primocss git development requires the latest version of node js https nodejs org en to setup here is a list of commands to help you get started bash npm install gulp build once complete open the build folder to see the newly created css you can override any of the global settings for primocss via settings defaults scss https github com primocss primocss blob master scss settings defaults scss and settings colors scss https github com primocss primocss blob master scss settings colors scss any new scss created can be tested by running the gulp linter and keeps all styles in check bash gulp test browser support using the latest technologies primocss is aimed at new browsers and will attempt to degrade gracefully to previous versions chrome latest firefox latest opera latest safari latest ie latest license code licensed mit https github com primocss primocss blob develop license md by richard mccartney http www github richmccartney | framework scss css scss-framework html pattern-library itcss mobile responsive flexbox-grid | front_end |
iot-dc3 | p align center img src dc3 images logo blue png width 400 br a href https gitee com pnoker iot dc3 stargazers img src https gitee com pnoker iot dc3 badge star svg theme gvp alt star img a a href https gitee com pnoker iot dc3 members img src https gitee com pnoker iot dc3 badge fork svg theme gvp alt fork img a br strong dc3 spring cloud iot strong p star 1 dc3 iot dc3 architecture dc3 images architecture1 jpg 1 1 dc3 x sdk sdk x x 1 2 dc3 x spring cloud x x x x java x x x x x kubernetes x docker 2 dc3 dc3 spring cloud 4 iot dc3 architecture dc3 images architecture2 jpg 3 iot dc3 wiki https doc dc3 site https doc dc3 site 4 dc3 gitee https gitee com pnoker iot dc3 issues i6i15g 6996 hk mars 5 gitee https gitee com pnoker iot dc3 issues i6ikal dc3 6 qq start our users dc3 images social png 7 main checkout main feature username description feature pnoker mqtt driver pr develop userid https doc dc3 site contributor 8 iot dc3 apache 2 0 https www apache org licenses license 2 0 html copyright | iot spring-cloud device-management data-collection multi-protocol mqtt rtsp java vue docker gateway opc-ua tcp socket plc dcs rpc visualization iot-dc3 | server |
AdvancedRelationalDatabases | advanced relational databases introduction as software engineers dealing with relational databases is a regular when building an application its easy to make a relational database and use it in our apps however many people are lacking when it comes to understanding and applying certain advanced concepts for relational databases and applying them hopefully the topics discussed will be able to shed some light on some database concepts you aren t familiar with such as transactions indexing sharding and more database transactions database transactions are a unit of queries or operations executed together to modify or access the database transactions end with a commit or a rollback the former means commiting the change permanently to the database while the latter rolls back the database to the state before the transaction began the two main points of using database transactions is to mkae it more robust by using rollbacks as well as keeping it consistent cncpt025 https user images githubusercontent com 62875631 189594267 3f73e0d0 7c47 429c a299 dffb02ebb29e jpg acid properties there are four properties to maintain transaction integrity that are abbreiviated to acid these properties are atomicity consistency integrity and durability atomicity atomicity states that a transaction must succeed or fail no in betweens if a commit is made that means the transaction has completed succesfully or if a failure occurs the transaction is rolled back consistency consistency is all about keeping the database consistent before and after the transaction this is usually achieved by user defined constraints isolation isolation means multiple transactions working concurrenty without interfering with each other database systems use isolation levels to state the degree of the isolation the transaction will be applying isolation levels wil be discussed later on durability finally durability is achieved by making sure modifications to the database are persisted one durability technique is the write ahead log wal which is writing changes to the db to logs and therefore commits arent actually affecting the database after the wal file reaches a certain limit a checkpoint is reached and so all changes are applied to the database read more about wal here https sqlite org wal html isolation levels isolation levels exist to choose the deegree in which we want our database to be isolated they are split into locking and row versioning locking isolation levels consist of read uncommited read commited repeatable read and serializable while snapshot isolation uses row versioning read more about locking vs row versioning here https docs microsoft com en us sql relational databases sql server transaction locking and row versioning guide view sql server ver16 however each isolation level faces a read phenomena that presents problems these phenomena are dirty reads lost updates non repeatable reads and phantom reads for a full guide on transaction isolation levels and read phenomena as well as shared locks and exclusive locks click here https levelup gitconnected com transaction isolation levels in ms sql guide for backend developers 6a5998e34f6c read uncommitted the first isolation level is read uncommitted where it allows transactions changes to be visible to each other before any commits this is possible due to the fact they do not use shared locks when reading data from the database however read uncommited faces issue with all the read phenomena making it the worst degree of isolation dirty reads dirty reads occur when data is read in one transaction while in another transaction data is updated and then so when transaction 1 rereads the data is changed the diagram below explains this well screenshot 2022 09 12 144815 https user images githubusercontent com 62875631 189645808 07fc0b7e 6dc7 419b 8b0f 8a4ac16ecf6c png read commited the second isolation level which is the default in most database systems is read committed this level only allows transactions to see data modified if it has been commited shared locks are aquired on queries that involve reading however the are released as soon as the query is executed read committed still faces problems llke lost updates and repeatable reads lost updates and non repeatable reads lost updates occur when a transaction updates a row but it is overwrited by another transaction screenshot 2022 09 12 155638 https user images githubusercontent com 62875631 189659532 69a82cd4 990f 454b 8266 8ca97e122e11 png non repeatable reads when data is read in one transaction then another transaction updates and commits then the first transaction rereads to see the data is not the same as the first read screenshot 2022 09 12 160143 https user images githubusercontent com 62875631 189660575 37cf27b8 42a9 4352 977e 72ace6d28b91 png repeatable read the third isolation level repeatable read fixes lost updates and non repatable reads in this level shared locks now release after commits so exclusive locks must wait for them to release to operate however this level still faces a problem which is phantom reads phantom reads phantom reads occur when one transaction reads data then another transaction inserts new data and the first transaction screenshot 2022 09 12 161102 https user images githubusercontent com 62875631 189662759 28cdd01c 1c57 4269 a291 07a834498c94 png serializeable the final locking isolation level is serializeable which adds shared lock now account for the range of data they are reading so they prevent modifications exclusive locks that affect the data in the range they are working with snapshot isolation using row versioning snapshot isolation works by persisting the version of the database when data is being modified by a transaction committed data is copied to tempdb and are given version numbers so when another transaction reads data there is no wait because of locking since it will receive the version of data from the most recent committed transaction serializeable vs snapshot isolation while serializeable and snapshot both achieve isolation they have significant differences serializeable level uses locking which helps keeps the database consistent and avoids read phenomena however its more prone to deadlocks which can hinder from concurrency snapshot uses row versioning which helps with increased concurrency however its main disadvantage is its increased tempdb usage from the storage of the row versions all in all if your thinking of building a system thats read and write heavy go for serializeable but if your system is just read heavy and not alot of writes go for snapshot screenshot 2022 09 12 195103 https user images githubusercontent com 62875631 189713777 afc36a34 2202 46fb a7bc 6acc58dd183b png cap theorem understanding the cap theorem is essential for designing how you want to design a distributed system cap stands for consistency availability and partition tolerance there are three ways to design your system when using the cap theorem ca consistent and available cp consistent and partition tolerant ap available and partition tolerant lets take a atm system as an example if using cp in the event of a partition network problem atm crash an atm might close the user out of the system since it doesnt want him to withdraw or deposit and create inconsistent data with the other atms on the other hand using ap an atm can stay active after a partiion and allow withdrawals and deposits and when the other atms come back online they will be updated with new changes finally we can use ca if we think a partition is rare to occur and if it does we can just fix the problem and get it back online later screenshot 2022 09 13 114410 https user images githubusercontent com 62875631 189855513 309e0bee e1e0 41b2 a955 508f09c244d6 png indexing we use indexing to increase the speed at which queries are executed when dealing with large databases when we create an index on a column we create a separate data structure that orders the data in way that helps search query efficiency screenshot 2022 09 12 200247 https user images githubusercontent com 62875631 189713796 141df822 6fdb 436e aaa0 0fe31025f15c png clustered vs non clustered index clustered indexes are made off of the primary key of a table and the leaf nodes for the index contains the row data non clustered indexes create a seperate data structure that have reference ids to the row data itself clustered indexes physically reorder the table data itself to match the index while non clustered indexes logical order does not match the physical order different types of index scans sequential scan regular in order table scan fast for fetching row from small table or high amount of rows from large table index scan scans index for pages that match query then uses reference point to fetch matching rows index only scan scans index for pages that match query but data needed is already in index so no need to jump to table faster than index bitmap index scan scans pages for matches with query condition and sets bitmap true for the pages that match then it just seeks out those specific pages in the heap index fragmentation index fragmentation occurs when modifications to the database cause blank spaces and page splits resulting in more io operations to scan the index for what we need index size increases because of blank spaces as well internal fragmentation free space cause by inserts or deletes that make the index store more data and results in more io operations to read logical fragmentation order of pages does not match physical ordering of pages making the searching not sequential in order to avoid index fragmentation we can use ever increasing decreasing keys as to avoid inserts that will cause page splits also avoiding updates on keys to avoid page splits using index fill factors can help us keep enough space in a page for more inserts anticipating the page split problem we can use the dbms to check for fragmentation if the fragmentation is less than 10 there is no need to fix index if there is 10 30 fragmentation reorganize index logical reordering if fragmentation reaches greater than 30 fragmentation we rebuild the index replication involves writing or copying data same data to different locations it can be from between hosts in different locations or storage devices on same host or cloud based host one technique for replication is using master standby which is having a master server that handles the main database operations and writes to standby nodes this is done to improve accessibility as well as achieving system fault tolerance and reliability master standby can be implemented synchronously or asynchronously synchronous replication means when the master recieves data operations it executes them and then applies them to standby immediately while asycnhronous replication wll write the changes to a log and apply them to standby at a later point partitioning partitioning is the process of splitting a database into multiple tables in order for queries to execute faster since there is less data to scan the goal of partitioning is to decrease read and load data response time horizontal vs vertical partitioning horizontal partitioning divides the table by rows ex id0 100tbl id101 200tbl screenshot 2022 09 13 150406 https user images githubusercontent com 62875631 189896320 3da9d49d b9d3 46e2 b126 f4d2eb2f65d9 png vertical partitioning divides the table by columns usually tries to seperate blob columns as to reduce accesss time on searches screenshot 2022 09 13 150424 https user images githubusercontent com 62875631 189896345 58264132 5c36 4011 bcbb 650abe1b0e79 png sharding partitioning database into multiple database servers with the same schema 1 woslzp8pkh8bwqdmi6jndw https user images githubusercontent com 62875631 189898573 fbea2475 9ee3 4ab2 9551 8d2871945293 png consistent hashing when sharding we use hashing to query on the correct server if we have for an example a hash function that hashes to 4 servers and we decide to add a new server the hashing function has to be adjusted for 5 servers and therefore we have to move data from server to server to match the new hash function which can be very costly by using consistent hashing we can reduce the amount of data we are moving around significantly screenshot 2022 09 13 153230 https user images githubusercontent com 62875631 189901783 3dfbffd2 0cdc 4ae5 a17a ed8614728cfa png like this when adding a new server we only need to move data from one node to another which is much better conclusion whether your a backend developer or a database administrator these concepts are the tools that are needed under our belt in order to be able to take action in whatever issue comes our way in our developing adventures from creating an index on a column to knowing when to shard or replicate your database these topics are essential for the fundamental understanding of advanced relational databases references big resource fundamental of database engines by hussein nasser https www udemy com course database engines crash course transactions https www tutorialspoint com dbms dbms transaction htm https fauna com blog introduction to transaction isolation levels https youtu be ctcao89fcqw acid https www geeksforgeeks org acid properties in dbms ref lbp https levelup gitconnected com transaction isolation levels in ms sql guide for backend developers 6a5998e34f6c https sqlite org wal html cap https www geeksforgeeks org the cap theorem in dbms https youtu be k yaq8ahlfa indexing https chartio com learn databases how does indexing work https www geeksforgeeks org difference between clustered and non clustered index text a clustered index is a of the rows on disk https www pgmustard com docs explain sequential scan https www spotlightcloud io blog tips for fixing sql server index fragmentation text external index fragmentation separated from the original page https blog devart com sql server index fragmentation in depth html https www sqlservercentral com forums topic index fragmentation and ssds text index fragmentation is bad on access is much 2c much faster https www beyondtrust com docs privileged identity faqs reorganize and rebuild indexes in database htm text reorganizing an ind sharding https youtu be ihnovzuzm3a replication https www stitchdata com resources data replication | server |
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Project-PESO-NET-Mobile | project peso net mobile getting started usage instructions and installation guide how to set repository create database pesodb create table tbl userrole id int not null auto increment role varchar 15 primary key id insert into tbl userrole role values staff insert into tbl userrole role values recruiter insert into tbl userrole role values seeker insert into tbl userrole role values admin create table tbl userlogin id int not null auto increment username varchar 255 email varchar 255 not null password varchar 255 not null primary key id create table tbl userinfo id int not null auto increment firstname varchar 255 not null lastname varchar 255 not null curraddress varchar 255 not null phonenumber varchar 20 not null fk roleid int fk userlogin int primary key id foreign key fk roleid references tbl userrole id foreign key fk userlogin references tbl userlogin id | server |
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ev-dashboard | open e mobility angular dashboard app summary the angular dashboard connects to the open e mobility nodejs server https github com sap labs france ev server to display the charging stations in real time the application features charging stations details and real time statuses charging sessions curves in real time charging stations remote control reboot clear cache stop transaction unlock connector charging station template management zero configuration user management badge management role management abac static energy management manually limit the charging station smart charging with assets fair sharing peak shaving cost management and phase balancing realtime asset management building battery solar panel billing with stripe complex pricing roaming integration gire hubject refunding sap concur simple statistics advanced analytics sap analytics car connector management get the car s data to optimize the charging session contact the author a href https www linkedin com in serge fabiano a420a218 target blank serge fabiano a installation install nodejs https nodejs org install the lts version clone this github project go into the ev dashboard directory and run npm install or yarn install use sudo in linux note on windows with chocolatey https chocolatey org do as an administrator powershell choco install y nodejs lts on mac osx with homebrew https brew sh do shell brew install node follow the rest of the setup below the dashboard configuration there is one template provided src assets config template json rename it to config json move this configuration file into the src assets directory edit this file you will set relevant config data in it connect to the central service rest server csrs the dashboard is served by a web server downloaded into the browser and will call the rest server to retrieve and display the data set the rest server url json centralsystemserver protocol http host localhost port 80 create and set a google maps api key ev dashboard requires you to setup a google api key https developers google com maps documentation javascript get api key restrict key once the key is created it must be enabled from the google console and the value must replace the one present in src index html in google maps section src https maps googleapis com maps api js key your key here libraries places language en script setup the recaptcha api key in order to call rest endpoints of ev server a recaptcha key is required refers to this link https www google com recaptcha admin create to create one then copy the client key in config json in user section json user maxpicturekb 150 captchasitekey google recaptcha key client start the dashboard server development mode shell npm start production mode first build the sources with shell npm run build prod next start the server with shell npm run start prod secured production mode ssl build the sources as above and run it with shell npm run start prod ssl integration tests to run integration tests you first need to start the ui and run the below command shell npm run test this will run all integraiton tests written with jest framework license this file and all other files in this repository are licensed under the apache software license v 2 and copyrighted under the copyright in notice notice file except as noted otherwise in the license license file please note that docker images can contain other software which may be licensed under different licenses this license and notice files are also included in the docker image for any usage of built docker images please make sure to check the licenses of the artifacts contained in the images | e-mobility charge-point-operation mobility-operation | front_end |
MobileAppDevBackend2 | mobileappdevbackend backend for mobile app development class android repo is at https github com deepp0925 mobileappdevelopment | server |
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